Paper ID #10807

Milestones as a Guide to Drafting Project to Improve the Application of Di-mensioning Specifications

Prof. Leonardo A. Bueno, Embry-Riddle Aeronautical Univ., Daytona Beach

Assistant Professor in the Engineering Fundamental department, teaching all the courses offered by thedepartment. His focus is on teaching and preparing students for the upper-level classes that follow in theireducational experience.

c©American Society for Engineering Education, 2014

Milestones as a Guide to Drafting Project to Improve the Application of Dimensioning Specifications

Abstract Students in first-year engineering often face this issue: a lack in their ability to apply the appropriate dimensioning specifications to design problems. Students need this skill in order to properly, and effectively, describe engineering solutions. This paper describes an attempt to engage learners by involving them in a design exercise where they are required to provide a detailed solution to a problem of their choosing. Through a series of milestones, learners are guided to: develop a design, check for completion, and conduct a final revision of their dimensioned sketches. It is proposed that through structured guidance, detailed feedback, and allowing students to invest effort and time in generating a solution of their interest, it will improve their dimensioning skills. This will be demonstrated through better-applied dimensions to their design solutions. Introduction The introductory course of Graphical Communications at Embry-Riddle Aeronautical University provides the student with the basic principles to improve and develop their visualization skills, 3D computer modeling abilities, and engineering design graphics as preparation for their engineering degree. The foundation for these areas is provided by a structure that consists of: exercise repetition during class, lab time, and structured feedback of homework. The culmination of the course is a final project requiring the use of several topics to reinforce learned course concepts and to strengthen the ability to make design decisions.8, 9 Traditionally, the final project encompasses the basics of Graphical Communication by requiring a set of drawings for a predefined object, leaving the CAD modeling, graphics and view layout decisions up to the student. This structure highlighted a problem, a lack in the students’ ability to correctly apply dimension specifications to open-ended designs. This paper describes an approach to the final project in which first-year engineering students create their own models and corresponding drawing sets. This exercise leads to the correct application of dimensioning specifications to the drawing sets as measured by the higher scores in their capstone assignment. Background This research attempts to investigate the difficulty in the adaptation of open-ended design problems in the Graphical Communication Curriculum. This is done with the use of a personal design project in which all design decisions are left to the student. This encourages a higher

level of involvement and brings to the forefront the need to be accurate in the use of proper dimensioning. This is a reasonable assumption given that the student is now aware that they are trying to communicate their own idea and not attempting to replicate a problem provided by the instructor. Initially, previous attempts at a student-chosen design were less successful. In practice, it was due to the scope of such projects; they were often too large for the time available during the semester, typically three weeks. In order to remedy this issue, the project format was restructured to span over a longer period of time, seven weeks, during the semester and to have increased instructor supervision of the student progress. Therefore, formative assessment milestones were built directly into the process. This would provide timely and appropriate instructor feedback and guidance during the early design stages and to the creation of the CAD models. If the student were to choose a problem that was too large or too simple, the issue could be resolved work starts. In addition, the formative quality of the feedback would allow the student to develop a solution properly addressing the requirements of their design.1 With the milestone format, it is expected that first-year students would be able to apply dimensioning with a greater degree of accuracy, as they would now possess detailed knowledge of the product and its complexities. That deeper understanding is necessary to apply critical to find the appropriate solutions in any skill-based learning situation.2 Approach Milestones were initially used as a guide for the final project in the Summer 2013. The four milestones were:

1. Proposal of the product to be designed, to include a small set of hand drawings and description of the anticipated size and configuration. A minimum of eight individual parts were required.

2. Presentation of detailed dimensions for each individual part. Hand sketches of the configuration including final sizing of each part with particular emphasis on mating dimensions.

3. Complete 3D CAD models of parts and assembly. 4. Final set of dimensioned drawings. Specifically:

a. Isometric of assembly b. Orthographic of assembly c. Exploded view of assembly d. Parts list e. Dimensioned views of each par

While the course content was the same, it should be mentioned that the semester length and profile of the students is slightly different than during the fall or spring terms. The summer term is six weeks long, and students meet 4 times a week. While shorter than the regular 14 weeks, it permitted the student daily access to the instructor and to focus on a narrower academic workload. As far as the students’ profile during the summer term, they were not all first-year freshmen.

Twenty-four (of the 34 students) were high schools juniors enrolled in Embry-Riddle ESPER (Engineering Scholars Program at Embry-Riddle) prior to their senior year. ESPER students receive six semester hours that count as standard university credits and toward their high school GPA. Regular incoming freshman are high school graduates who either have declared an Aerospace Engineering degree or are enrolled as still exploring. While there were differences in the academic status of the students, there is no significant difference in the profile of the students. The rising seniors are only one year away from attending college and being of similar age, they showed similar level of maturity. Also, they are academically minded students who were recommended by their corresponding high-school counselors and who are aware that they are enrolled in courses of higher education. Compared to the anecdotal experience from previous semesters, the results were encouraging. 34 students formed 13 teams and all teams were able to create and complete all parts, assemblies and drawings for their project. Final project grade was high with 46% scoring above 90% and zero failures. While there was a significant reduction in dimension application errors, it was not completely eliminated, with 8 of the final projects still exhibiting a noticeable number of missing dimensions. Design This paper presents the application of milestones as guide to the final project in order to improve the application of dimensioning specifications during the Fall 2013 semester. This term was 14 weeks long and the majority of the students were first-year, first-semester engineering students, the majority of which are in the Aerospace Engineering program. As the purpose of these intermediate markers is to allow timely feedback, the original milestones were not changed. They provided timely feedback to facilitate continuous progress. However, in the summer term it was observed that a quality control check could be implemented to further improve student performance. For the Fall term a review day with self-evaluation of the final drawing set was introduced 10 days prior the final due date. This increased the milestones from four to five:

1. Proposal of the product being designed, to include a small set of hand drawings and description of the anticipated size and configuration. A minimum of eight individual parts were required.

2. Presentation of detailed dimensions of each individual part. Hand sketches of the configuration including final sizing of each part with particular emphasis on mating dimensions

3. Complete 3D CAD models of parts and assembly. 4. Self-evaluation of the final drawing set. The final rubric and a list of common errors

were provided. In addition, the instructor and teaching assistants were able to consult individually if the students require it.

5. Final set of dimensioned drawings. Specifically: a. Isometric of assembly b. Orthographic of assembly

c. Exploded view of assembly d. Parts list e. Dimensioned views of each part

The results for the Fall semester are summarized in the following section showing the results for all students. Results The assessment was done through a rubric, developed by the author and provided in Appendix A, with a specific scoring component for dimensioning. A description of each item in the rubric is included in the appendix. This is the same rubric that was used during the summer semester and it allows for a baseline when comparing scores. The instructor also remained the same for both terms. There were a total of 54 students that created 26 final projects. Several projects were developed by individuals and no group was comprised of more than three members. The distribution in size and scores are shown in Table 1.

Table 1. Final and dimensioning score distribution based on group size Group Size Number of Groups of a

size Dimensioning Score

Average Final Project Score

Average 1 6 74 85 2 13 78 82 3 7 89 93

Performance was much better in groups of three members however the relationship between group size and individual learners' abilities was not studied. Table 2 lists the dimensioning score and its relation to the project final letter grade. This is an area of interest since this correlation shows that, as a whole, the higher the dimensioning score, the higher the total project grade.

Table 2. Summary of results of dimensioning scores Dimensioning score % Project Letter Grade Number of Groups

Above 90% A(9) B(1) 10

Above 80% A(2) B(3) C(1) 6

Above 70% A(1) B(1) 2

Above 60% B(2) C(2) 4

Above 50% C(2) D(2) 4

The rubric from the summer term was the same as the one implemented in the fall, dimensioning score distribution can be compared and it is shown in Figure 1.

Figure 1. Dimensioning score vs. percentage of projects that achieved a particular grade

Figure 1 shows that the number of students with a passing grade in the dimensioning section improved significantly from summer to fall. The score was higher during the Fall 2013 semester at 70% scoring average or above versus 54% in the Summer 2013 semester for the dimensioning application score portion. While the groups of students are very similar, external causes could have had an effect on the dimensioning scores. First, there is a slight difference in academic background as the rising seniors are still a year away from enrolling in higher education. However, they are highly motivated students who showed an interest in the sciences and engineering. A more likely cause for the difference is the actual time availability, with summer term students completing the project in 4 weeks as oppose to 8 weeks available to the Fall students. The total final project grade did not follow the same pattern as the dimensioning score. In this instance, a few more projects did score a total that was lower than a B. This would not be totally unexpected given the increased in class size. The comparison between summer and fall is shown below in Figure 2.

0  5  10  15  20  25  30  35  40  45  50  

Above  90%   Above  80%   Above  70%   Above  60%   Above  50%  

Percentage  of  Projects  

Dimensioning  Score  

Summer  2013  

Fall  2013  

Figure 2. Final project grade vs. total projects that achieved that grade

As with the previous term, common errors were also recorded if they occurred in a project more than five times. The most common issues were grouped into categories: Missing Dimensioning, if a required dimension is not provided, Improper Dimension Location, if the dimension is located in the wrong place or view, and Incorrectly Displayed Units, if, for instance, feet were displayed instead of inches. Given the difference in class size, Table 3, lists the number of projects containing these errors as a percent of the total for the corresponding semester.

Table 2. List of most common errors Type of Error Percentage of Projects Containing the Error

Summer 2013 Fall 2013

Missing Dimensioning 62% 19%

Improper Dimension Location 46% 57%

Incorrect Displayed Units 46% 12%

There was a reduction in the cases with several missing dimensions however improper location still remains an issue across all projects. The level of complexity in the final project varies, but students are free to choose a design that interests them. Figures 3 and 4 show sample extracts from a final project in which a student wanted to model a truck toolbox. Figure 3 shows how the parts were used in the assembly and one of the internal subassemblies built to support their design. In figure 4, the dimensioning for one of the parts of the subassembly is shown.

0  5  10  15  20  25  30  35  40  45  50  

A   B   C   D  

Percentage  of  Projects  

Final  Project  Letter  Grade  

Summer  2013  

Fall  2013  

Figure 3. Portion of exploded view of final project product (left) and of internal

subassembly (right)

Figure 4. Dimensioned view of single element

Conclusions The level of personal involvement of the students in their project appears to increase their commitment to complete the project in a professional and detailed manner. During both terms, most teams were able to complete projects with a grade of C or better. Students do benefit from the internal knowledge of the complexities of their own project. They were able to complete with their projects, including all drawing sets in the time required. The addition of the quality control milestone provided a way of catching major mistakes or oversights prior to finishing the drawing set. This is shown as an improvement in the dimensioning application aspect of creating a drawing set. In this case, 70% of the class during the fall semester was able to achieve a grade of C or above as opposed to the summer term where only 54% did.

Given the benefits in student performance this format will continue to be used during the Spring 2014, the conditions and milestones will be kept the same as in the fall semester. References [1] Leahy, K. (2012). Promoting best practice design intent in 3D CAD Modelling: Feedback as a constructivist paradigm of teaching and learning. Department of Design and Manufacturing Technology. University of Limerick. [2] Wright, D. (2007). The Decision Pattern: Capturing and Communicating Design Intent. Computer Science Department North Carolina State University Raleigh, NC USA. [3] Gerber D. and Forest F. (2011) Teaching Design Optioneering: A Method for Multidisciplinary Design Optimization. CIFE Technical Report #TR203 June 2011. Stanford University. [4] Rynne A. and Gaughran W. (2007) Cognitive Modelling Strategies for Optimum Design Intent in Parametric Design. American Society for Engineering Education AC 2007-2132 [5] Tate, Derrick et al. (2010) Matching pedagogical intent with engineering design process models for precollege education. Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2010), 24, 379–395. [6] Condoor, Sridhar et al (2008) The Art of Design Modeling – Teaching Freshmen. American Society for Engineering Education AC 2008-2094 [7] McInnis, J., Sobin, A., Bertozzi, N., Planchard, M. (2010) Online Working Drawing Review and Assessment. Engineering Design Graphic Journal, Vol. 74, No. 1 [8] Devine, K. (2012) Dimensional Tolerances: Back to Basics. Engineering Design Graphic Journal, Vol. 76, No. 1 [9] Lamb, C. and Kurtanich, D. (2007) Drafting the Basics. Engineering Design Graphic Journal, Vol. 71, No. 3

Appendix A

Rubric for evaluation of final project:

FINAL PROJECT RUBRIC NAMES: ______________________________ ASSEMBLY: _____________/ 10 ELECTRONIC FILES: _____________/5 ISO, ASSEMBLY: _____________/5 ORTHO, ASSEMBLY: _____________/10 EXPLODED: _____________/10 PARTS LIST: _____________/10 INDIVIDUAL PARTS: DIMENSIONING _____________/30 VIEWS _____________/10 PRESENTATION (TITLE BLOCK, PRINTING,NEATNESS) _____________/10 TOTAL _____________/100

What are the expectations of each segment in the rubric?:

- ASSEMBLY o Individual parts constrained properly o Interference issues?

- ELECTRONIC FILES o Are all files’ dependencies correct?

- ISO, ASSEMBLY o Is the isometric view of the assembly correct? o Are line type conventions correct?

- ORTHO, ASSEMBLY: o Are the orthographic views of the assembly correct? o Are line type conventions correct?

- EXPLODED: o Is the exploded view shown correctly? o Are all part labeled so that they match the parts’ list?

- PARTS LIST: o List the parts’ names, quantity and number (to match exploded view)

- INDIVIDUAL PARTS: DIMENSIONING: o Dimension missing? o Dimension applied correctly?

- INDIVIDUAL PARTS: VIEWS: o Are the views correct or sufficient?

- PRESENTATION: o Overall presentation of the drawing set. Title block format, printing scale, page

order, proper spelling.

Paper ID #9242

Solid Modeling Strategies – Analyzing Student Choices

Holly K. Ault Ph.D., Worcester Polytechnic Institute

Holly K. Ault is Associate Professor of Mechanical Engineering at WPI. She serves as director of theMelbourne (Australia) Project Center and co-director of the Assistive Technology Resource Center. Shereceived her BS in chemistry, and MS and Ph.D. degrees in Mechanical Engineering from WorcesterPolytechnic Institute in 1974, 1983 and 1988 respectively.

Professor Ault has advised off-campus project students in London, Copenhagen, Stockholm, Windhoek(Namibia), San Jose (Costa Rica), Washington DC, Boston, Modesto (CA), and Melbourne. In the fallof 2001, she was invited as the Lise Meitner Visiting Professor, Department of Design Sciences, LundTechnical University, Lund, Sweden. Prior to teaching at WPI, she worked as a Manufacturing Engineerfor the Norton Company in Worcester, MA, and Product Development Engineer for the Olin Corporationin East Alton, IL.

Professor Ault’s primary teaching responsibilities include undergraduate and graduate level courses incomputer-aided design, mechanical design and rehabilitation engineering. Her research interests includecomputer aided mechanical design, geometric modeling, kinematics, machine design, rehabilitation engi-neering and assistive technology. She is a member of ASME, ASEE, ISGG and Tau Beta Pi.

Linjun BuKejiang Liu, Worcester Polytechnic Institute

c©American Society for Engineering Education, 2014

Solid Modeling Strategies – Analyzing Student Choices Abstract

There is an increasing trend in solid modeling instruction to include not only procedural knowledge but also strategic knowledge when teaching students how to build solid models. In order to evaluate the many choices made by students as well as experts in their modeling procedures, and to compare different types of solid models used in these studies, it is necessary to measure various parameters associated with model complexity and “goodness” of the solid model. In this paper we will propose metrics for evaluating solid models such that different modeling strategies can be compared. Furthermore, we will compare the models created by students who are given different strategic instructions for modeling, as well as the results of change exercises implemented in upper division solid modeling courses.

Introduction

Solid modeling instruction must include not only declarative and procedural knowledge but also strategic knowledge when teaching students how to build solid models1-4. In the context of solid modeling, declarative knowledge includes facts such as the types of features that can be used to build a solid model, the types of geometric constraints used to control sketches, dimensioning rules, etc. Most current engineering graphics texts include this type of information5-8. Procedural knowledge focuses on how to perform various functions or utilize the commands in a particular solid modeling system. This type of information can be found in any of the typical introductory CAD tutorial texts or manuals 9-11, as well as online learning available from the software vendors 12,13 or independent sources such as YouTube 14.

Effective use of CAD systems also requires the acquisition of strategic knowledge such as selection of solid modeling alternatives and proper use of modeling constraints to capture design intent 2-4. Two schools of thought dominate the strategic approaches used by instructors as well as practicing designers – efficiency and flexibility. An efficient model is one which can be created quickly by the designer and results in a smaller file. A flexible model is more easily changed by the designer or others who utilize the model in downstream applications. Both strategies seem to be widely used, although it is not clear whether one approach is better than another, or under what circumstances each approach is favorable. Bhavnani et al.15 and Hamade et al.16 favor fewer, more complex features, as these parts can be modeled more quickly. On the other hand, Rynne & Gaughran4 and Chester2,17 recommend simpler sketches for parts which are more easily modified. Wu18 stresses that the best strategy for modeling more complex parts may not result in the smallest number of features, and that overall efficiency of model use depends not only on the speed with which the original model is made, but also the ease of changing the model. Ault & Giolas19 found that practicing designers used both strategies. Furthermore, these designers rationalized their selection of modeling strategies as a combination of habits or personal preferences, company standards, and capabilities or limitations of the software. Johnson et al.20-23 add another consideration – easy to understand – since the model will be used by others during the product development. Using clearly named features rather than the default generic labels and logical grouping of features can facilitate understanding of the model by others.

Solid modeling strategies are often described as a means to “capture design intent”, but the interpretation of this phrase is rarely explicitly stated. Barnes et al.24 describe design intent as the intelligence built into the solid model to control the behavior of the part when subjected to changes or alteration. However, it is difficult to predict what types of changes a part may undergo during product development. New part design often encounters both dimensional and topological or feature changes. Selection of the dimensions as well as part features should reflect the important functional requirements of the part.

Most solid modeling exercises found in CAD tutorial texts are presented as either orthographic or isometric drawings of existing parts. Kirstukas25 explains that design intent can be inferred by careful inspection of the part drawing. The solid model should utilize only those dimensions (parameters) found on the drawing, as these dimensions were selected by the original designer to capture functional requirements of the part. The modeler should not need to compute additional dimensions to create the solid model, nor should dimensions be repeated in the model. The modeler should be able to reproduce the part using the given dimensions and suitable geometric and algebraic constraints. In Ault & Giolas’19 interviews, practicing designers identified a correlation between the modeling parameters and the dimensions on the 2D drawings. Devine & Laingen26 propose assessment of student models by comparing the list of parameters to the dimensions on the given drawings. Numerous instructors report that assessing student models should include changing the model parameters or “flexing” the model.1,19-28

Salehi and McMahon29 surveyed CAD users in the automotive industry and found that the users identified a lack of methodology to select the necessary parameters and associative relationships in their solid models. This strategic knowledge requires higher level thinking skills associated with decision making, or knowing about available alternatives and how to choose between these alternatives. Thus, it would seem that experience is an important aspect in the development of modeling strategies.

Barnes et al.24 suggests that many of the exercises presented to students are in the form of “elegant solutions” which present essentially one single obvious modeling approach. Fortunately, even very simple parts such as those presented in standard graphics texts can be modeled using different strategies. In planning a part model, the designer must decompose or “featurize” the part to be created in the solid modeling system. Two common strategies for modeling simple parts involve decomposition into features based on either additive or subtractive approaches.30

Metrics for Evaluating Solid Model Part Complexity

It is important to choose parts for CAD instruction that present increasing levels of complexity as well as alternative feature selection. How complex is the part? Is the model an “elegant solution” or are there many possible solutions? How much do these different solutions affect the efficiency or flexibility of the model? Each of these questions must be considered when choosing parts which will enhance students’ strategic modeling skills.

One measure of the complexity of a model is the number of features in the model tree. However, a simple count of the number of features in the model tree may be a poor measure of the model complexity. Features can be grouped into three categories: sketched features such as extrusions and revolves, form features such as holes, and edge features such as rounds and chamfers. In

addition, geometry can be duplicated using patterns and mirror options. Complexity of sketched features can vary based on the number of entities in the sketched profile and the number of sketches (for swept and blended features). Edge features may be applied to multiple edges, in sets with common parameters. Johnson et al. include the following counts in their comparison of different modeling strategies: number of features, average number of entities in each sketched feature or number of edges in round and chamfer features, number of patterns, and number of mirror features20-23. Since all of their studies utilized the same part, these factors were each considered separately in the modeling strategy comparison, and there was no need to consider the complexity of the part itself.

Another measure of complexity could be the number of parameters or dimensions in the model. However, as noted previously, some dimensional constraints could be replaced with geometric constraints on the same part, depending on the design intent. Kirstukas25 demonstrates this with two different versions of the same part, one of which has eight dimensions and the other nine, as seen in Figure 1. Other non-numeric feature parameters might include such characteristics as the terminal conditions for protrusions and holes.

Figure 1. Part with different dimensioning schemes and parameter count (Kirstukas25)

Datum features may or may not be included in the feature count. The need for datum features may be due to differences in modeling systems; for example, some systems will recognize the axis of any circular edge whereas others will need to have this axis explicitly defined before using it as a reference for other geometry construction. Ault & Giolas19 noted that some designers prefer to base sketches on datum planes rather than existing planar faces; this strategy has also been promoted as a means to unlink the associative parent/child relationships in a solid model. If the goal is to measure the complexity of the basic geometry, then the datum features would only be used to facilitate different modeling strategies or system requirements and do not affect the basic geometric form of the part and should therefore not be included in the complexity measure.

Based on these observations, the authors propose the following equation for computing the part complexity index (CI):

𝐶𝐼 = �𝐸𝑖

𝐹𝑆

𝑖=0

+ ��𝑁𝑖𝑗

𝑆

𝑗=1

𝐹𝐸

𝑖=0

+ �𝐶𝑖

𝐹𝐻

𝑖=0

+ �𝐶𝐼𝑖

𝐹𝑀

𝑖=0

+ �𝐶𝐼𝑖

𝐹𝑃

𝑖=0

(Eq. 1)

Where:

FS = number of sketched features (extrude, revolve, sweep, blend) Ei = number of entities in the associated sketches for the ith sketched feature FE = number of edge features S = number of edge feature sets Nij = number of selected edges for the jth edge feature set within the ith edge feature FH = number of (individual) hole features Ci = hole complexity factor for ith hole (simple holes = 1, countersunk or counterbored = 2) FM = number of mirror features -1 FP = number of pattern features -1 CIi = Complexity index of features patterned or mirrored in the ith pattern or mirror feature

The proposed algorithm has been applied to parts utilized by various researchers in their studies that have been modeled using alternative modeling strategies. Kirstukas25 uses a simple plate with hole features and standoffs, shown in Figure 1. Johnson17 uses a similar baseplate part with more slots, mirrored or patterned features and standoffs, shown in Figure 2. The goal is to develop a measure of part complexity that is independent of the modeling strategy used.

Figure 2. Baseplate part used for modeling strategy comparisons by Johnson et al.20-23

Each of the parts was modeled using a minimum number of features with complex sketches, including rounds in the base sketch (K1 and J1). This strategy conforms to the approach promoted by Bhavnani et al.15 and Hamade et al.16 for efficiency. The model tree and part K1 are shown in Figure 3. The model tree and base sketch for part J1 are shown in Figure 4. Dimensions and constraint symbols have been hidden for clarity. The complex sketch for this base feature contains 56 entities; the model contains only 4 sketched features, two copy features, one hole and one edge feature.

Figure 3. Model tree and K1part

Figure 4. Model tree and base feature sketch for Johnson Baseplate J1 strategy

The parts were also modeled using a larger number of simpler features (K2 and J3), to provide more model flexibility for alterations, as recommended by Rynne & Gaughran4 and Chester2. Figure 5 shows the model tree and base feature for this modeling strategy. The simple sketch for this base feature contains only 4 entities; the model contains six sketched features, five copy features, one hole and four edge features.

The Johnson part was also modeled with an intermediate strategy (J2), which contained 5 sketched features, 4 copy features, one hole and one edge feature. Two additional parts were modeled to determine whether the feature types would affect the complexity index calculations. Part A, shown in Figure 6, was modeled using only extrusions for A1, and a combination of revolve and extrude features for A2. Part B, shown in Figure 7, was modeled using only extrusions for B1 but included a blend feature for B2. Results of the complexity calculations for

parts modeled with these alternative strategies are shown in Table 1. Note that parts K and J were modeled using the same collection of features for the chosen geometry (largely extrusions). For these parts, the CI remains unchanged, as the variations in the number of features and entities in each feature results in the same product sum. However, in the case of part A, substituting a revolved feature in place of an extruded feature increases the CI, as there are four entities in the rectangular sketch required to revolve a cylinder as compared to the single circle used to extrude the same cylinder. Similarly, use of the blend feature instead of an extrusion increases the CI for part B.

Figure 5. Model tree and base feature sketch for Johnson Baseplate J3 strategy

Figure 6. Part A with two modeling strategies

Figure 7. Part B with two modeling strategies

Table 1. Measures of part complexity

FS FE FH FM FP CI K1 2 1 2 5 0 30 K2 3 1 2 3 1 30 J1 4 1 1 0 2 101 J2 5 1 1 0 4 101 J3 6 4 1 1 4 101 A1 4 1 3 2 0 26 A2 4 1 1 2 0 34 B1 9 2 3 1 0 61 B2 8 2 3 1 0 66

The Complexity Index measures can be compared to the data collected using Johnson’s comparators of number of features, average number of entities in each sketched feature or number of edges in round and chamfer features, number of patterns, and number of mirror features, as shown in Table 2. Johnson et al.20-23 observed that experts’ models had fewer, more complex features, such as models K1 and J1.

Other factors associated with the quality of a modeling strategy include the correct placement of the part origin and orientation of the model within the global coordinate system. Proper placement of the part facilitates use of the global datum planes to capture symmetry and creation of correct drawing views. Feature order and feature termination characteristics are also important to capture design intent and create a robust, flexible model18,20,25. These factors are not a measure of the part complexity but do contribute to the robustness and usefulness of the model in downstream applications.

Table 2. Johnson’s comparative part measures

Part # Features Avg. # edges/ sketch feature

Avg. # edges/ edge feature # Patterns # Mirrors

K1 10 4 2 1 3 K2 10 6 2 0 5 J1 9 17.75 16 3 0 J2 11 7.8 16 8 0 J3 22 4.17 6 4 2

Prior Research on Student Change Modeling Strategies

Student choices often depend on experience. In particular, modeling for flexibility is often overlooked by students, who are usually taught simply to duplicate a given part geometry. Part modifications, if assessed, usually involve dimensional changes only. Students are seldom exposed to exercises that require part feature modifications, and therefore have little experience in inspecting, assessing and making changes to existing models. These students will utilize a “brute force” approach rather than a strategic approach to making necessary design changes. Johnson et al.20-23 have conducted some studies to assess student modeling strategies associated with change, including both dimensional and topological changes. This paper extends the investigation using the same part, shown in Figure 2, including Johnson’s changes as seen in Figure 8. Dimensional changes are highlighted in yellow. Topological changes include removal of two slots, changing two round features to chamfers, and changing the geometry of two of the standoff features (circled in red).

Figure 8. Dimensional and topological changes to Johnson’s baseplate model20-23.

As a measure of the change, Johnson recorded the numbers of new features, deleted features, changed features and unchanged features, as well as the percentage of retained features (changed or unchanged). In a series of studies, the researchers compared modeling strategies of students and experienced designers in creating the original part as well as making modifications to different versions of the original part.

Observing Student Change Strategies

In order to study student modeling strategies for creating and altering models, two classes were selected for comparison. One course is a second level advanced CAD course for upper division mechanical engineering students using PTC Creo (32 students) with a focus on extending the students’ modeling repertoire and using the solid models in simulation and design analysis applications. The second course was a graduate level CAD course focusing on geometric modeling with more emphasis on theory and mathematics (19 students). The graduate students were allowed to use any available CAD system based on their past experience; most chose SolidWorks, with a few students using PTC Creo. Students in both courses participated in a lab exercise similar to the Johnson studies20-23. The students were assumed to be familiar with the modeling software and were able to justify the strategies they used during the experimental lab.

As in the Johnson study17, the graduate students were instructed to build the part shown in Figure 2, with half of the class instructed to model based on speed and efficiency and the remaining half of the class instructed to build a robust, flexible model that would accommodate design changes. Students were given one hour to complete the initial model, and completion times were recorded. In the second part of the lab, the students were asked to make design changes as shown in Figure 8. Unlike the Johnson study20-23, the second phase of the lab utilized parts that were modeled by the instructor rather than models created by their peers. Half of the students in each group were given easy-to-change parts (similar to J3) and the other half were given efficiency parts (similar to J1), both modeled by the instructor. Students were again given one hour to complete the model changes and times for completion were recorded.

For the undergraduate course, the exercise contained 4 phases, one before the lab and three during the lab period. In phase one, the students were told to create a model of the part shown in Figure 2 using their own strategies, and no other information was provided to influence their modeling strategies. Johnson17 reported that many of his students were unable to complete the modeling task within the allotted time (50 minutes), so we elected to allow the students to model the parts outside of class to allow as much time as needed. Modeling times were not recorded. In phase two, during the first hour of the lab, the students were given models of the same part created by the researchers using three different strategies (J1-J3). The given models were created using features for flexible modeling (J3), efficient modeling (J1) and a combination of flexible/efficient modeling (J2) as noted above. The students were told to inspect and study the models and compare to their own model. Some hints were given to help the students assess all the models, such as feature numbers, complexity of the sketches and different options for specific features. In phase three, the students were told to modify their own parts according to Figure 8. The students were assumed to use what they learned in the inspection phase to modify their model with suitable features. And in the last phase the students were told to choose one of the given models from phase two, with the chosen model which was most different from their own model, and to modify according to Figure 8. The times were recorded during phase three

and four as well. After the lab, the students were asked to answer a few multiple choice questions and also write a brief report about what they learned during the lab.

To compare the data, the students were sorted by their self-reported level of experience using CAD software.

Level 1: introductory CAD courses only Level 2: introductory CAD courses plus infrequent use in class projects Level 3: 1-2 years of frequent use in an academic setting Level 4: extensive experience, including industry or internship

By comparing these different levels of students with their own models and modified models, it is possible to explore and study how students justify their modeling strategy when they create and modify a part.

Student Approaches to Original Part Modeling

In accordance with the strategies recommended for a more robust, flexible model, one would expect the models to include more, simpler features as well as hole features, mirrors, patterns and edge features whenever possible. Complex sketches should be avoided to facilitate model changes. For a more efficient model, one would expect fewer features.

Table 3 shows results for the original models created by both graduate and undergraduate students, along with the J1 (efficiency) and J3 (change) models created by the researchers. Students with industry experience tended to create models that are more efficient, with fewer sketched features and fewer copy features, indicating that their sketches are more complex. These experienced students also used more edge features, suggesting that they recognize these as secondary elements of the geometry which are more easily modeled as 3D “algorithm” features as compared to 2D sketch features. It is interesting to note that these strategies are similar to those observed by other researchers who have investigated modeling strategies of expert CAD modelers in industry. The average number of features for all student groups was comparable to the number of features in the instructor-modeled J3 part, with slightly higher number of sketched features and slightly fewer copied features.

Table 3. Part model creation statistics for student models

Experience Level # students Avg. Total #

Features

Avg. # Sketched Features

Avg. # Copy Features

Avg. # Edge Features

1 10 13.8 6.0 3.7 1.8 2 12 12.8 5.6 4.5 2.2 3 4 15.0 6.3 4.5 2.0 4 6 12.0 5.0 3.0 2.7

Grads (speed) 6 10.7 5.0 0.8 3.3 Grads (change) 8 14 5.9 1.9 3.7

J1 -- 7 3 2 1 J3 -- 12 5 5 1

Upon inspection of the original models created by the graduate students, we concluded that these students typically used the same strategies regardless of instructions given. It may be noted that the nineteen students ranged widely in experience, including five students who had only a basic introductory CAD course and infrequent or occasional use, and five students with extensive work or project experience; the distribution of student experience between the groups was not controlled.

Student Strategies for Part Changes

Change exercises also demonstrated differences in outcomes based on the skill levels of the students as well as the starting point for the given change. Results for the undergraduate students using the instructor-provided models are shown in Table 4. Note that only one student chose the J3 model to change. The J1 model originally contained only 7 features, as compared to the J2 model with 12 features. The J1 model is most unlike the original models created by the students, which had an average of 13 features. With fewer features available in the J1 model, the number of changed and deleted features was lower than for the J2 model. Interestingly, the number of new features needed for the J2 model is also higher. While there are some differences between students with varying skill levels, the primary differences are based on the part selected for modification.

A more detailed inspection of individual part files is needed to determine the types of changes made by the students, whether these changes are simple dimensional variations, or changes to sketch entities or other feature characteristics, and how their part modification strategies vary based on the types of topological changes needed.

Table 4. Part modification statistics for undergraduate students

Experience Level or Part

Chosen

# Students

# New Features

# Deleted Features

# Changed Features

# Unchanged

Features 1 10 2.9 2.1 5.0 2.8 2 12 4.25 2.92 6.83 3.17 3 4 4.75 3.0 5.75 2.5 4 6 3.5 4.0 5.5 2.5 J1 22 2.77 1.73 4.68 2.59 J2 9 5.89 5.78 8.67 3.56 J3 1 6 2 7 2

Student comments were interesting in that the exercise received mixed responses regarding sketch complexity and preferred modeling strategies. One student recommended “leaving whatever complexity I can out of sketches and incorporating them into additional features when possible so they can be modified easier” whereas another observed that “round and chamfer features within the sketch are both slightly easier to create and to modify”. When asked whether they were influenced to change their modeling strategies based on the experience of inspecting and modifying these parts, some students suggested very specific strategies:

• “In future models I will be sure to construct my rounds and chamfer features at the end of my modeling process.”

• “The main strategy I am going to do is naming the features I make. I am also going to start using chamfers and rounds as a feature.”

• “In the future, I will try not to group up different features in a mirror so that I could change the part if necessary.”

• “After seeing how useful mirror is in the past few labs, I will certainly employ more use of that feature.”

A few reflected on the situational aspects of modeling strategy: “I would certainly use different modeling strategies depending on the intended future of a particular part file.” Some of these comments may reflect the students’ inexperience with changing models, as it was observed that many students immediately opened the sketches to make simple dimensional changes rather than using the edit function for part parameters.

Conclusion

Instruction of students regarding part modeling strategies to capture design intent continues to evolve, even as the solid modeling systems become more complex and users in industry develop best practices. Intentional exercises to make students aware of the need for careful part planning should include more strategic discussions regarding the uses of part models and alternative methods. Models used for these exercises need to be sufficiently complex to challenge students’ ability to decompose parts and consider alternative modeling strategies. There is no single correct answer regarding part modeling strategies, and students must rely on experience and situational decision making to build robust solid models.

Bibliography 1Ault, H.K. (2013), A Comparison of Manual vs. Online Grading for Solid Models, Proceedings of the 2013 Annual Conference of the American Society for Engineering Education, Atlanta, GA, June 23-26, 2013. 2 Chester, I. (2007) Teaching for CAD Expertise. International Journal of Technology and Design Education, Volume 17, Number 1 (2007), 23-35.

3 Menary, G. and T. Robinson (2011). Novel approaches for teaching and assessing CAD. International Conference on Engineering Education, Belfast, N. Ireland, 21-26 August 2011.

4 Rynne, A., and W. Gaughran (2012). Cognitive Modeling Strategies for Optimum Design Intent in Parametric Modeling. Computers in Education Journal, Vol. 18 No. 1, pp. 55-68.

5 Lieu, D. and S. Sorby (2008), Visualization, Modeling, and Graphics for Engineering Design, Delmar Cengage Learning.

6 Bertoline, G. R. & Wiebe, E. N. (2002). Fundamentals of Graphics Communication. 3rd Edition. Boston: McGraw-Hill.

7 Giesecke, F. et al (2000). Engineering Graphics, Prentice Hall, Upper Saddle River, NJ. 8 Luzadder, W. and J. Duff (1993). Fundamentals of Engineering Drawing, Prentice Hall, Upper Saddle River, NJ.

9 Toogood, R. (2012). Advanced Tutorial for Creo Parametric 1.0 & 2.0, Schroff Development Corp.54 Rider, M. (2013). Designing with Creo Parametric 1.0. McGraw-Hill, New York.

10 Planchard, D. and M. Planchard (2012). SolidWorks 2012 Tutorial. Schroff Development Corp.

11 Rider, M. (2013). Designing with Creo Parametric 1.0. McGraw-Hill, New York.

12 Parametric Technologies Corporation (PTC) (2012). http://www.ptc.com/ Accessed 11 July 2012.

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14 YouTube (2013). http://www.youtube.com/ Accessed 03 January 2013

15 Bhavnani, S. K., John, B. E., & Flemming, U. (1999). The strategic use of CAD: an empirically inspired, theory-based course. Proceedings of the CHI99 Conference: CHI is the Limit-Human Factors in Computing Systems. Pittsburgh, PA, USA. 183-190. New York, USA: ACM. 16 Hamade, R. (2009). Profiling the Desirable CAD Trainee: Technical Background, Personality Attributes, and Learning Preferences. ASME J. Mech. Des. Vol. 131.

17 Chester, I. (2008). 3D-CAD: Modern Technology – Outdated Pedagogy? Design and Technology Education: An International Journal 12, 1, p. 7-9. 18 Wu, J. (2009). A Study of the Learning Models Employed by Industrial Design Students When Learning to Use 3D Computer-Aided Design (CAD) Software. Int’l. J.Arts Education, retrieved from ed.arte.gov.tw Feb. 15, 2014. 19 Ault, H.K. and D.T. Giolas (2005). An investigation of solid modeling practices in industry. Engineering Design Graphics Journal. 69(1): p. 34-43.

20 Johnson, M. and R. P. Diwakaran (2011), CAD Model Creation and Alteration: A Comparison between Students and Practicing Engineers, ASEE Annual Conference, 2011. 21 Johnson, M. (2009). Design under Alternative Incentives: Teaching Students the Importance of Feature Selection and Organization in CAD. Proceedings Annual Conference of the American Society for Engineering Education. 22 Diwakarana, R. and M. Johnson (2012). Analyzing the effect of alternative goals and model attributes on CAD model creation and alteration. Computer-Aided Design 44: 343–353. 23 Johnson, M. and Diwakarana, R. (2010). Examining the Effects of CAD Model Attributes on Alteration Time and Procedure. Proceedings of the ASME 2010 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference. Downloaded 24 Sep 2012. 24 Barnes, S., E. Wiebe and T. Branoff (2011). The Effects of Worked Examples on CAD Performance: An Application of the Four-Component Instructional Design Model to Cad Instruction. 25 Kirstukas, S. (2013). A Preliminary Scheme for Automated Grading and Instantaneous Feedback of 3D Solid Models. Proc. Midyear Conf. Eng. Design Graphics Division of the Am. Soc. for Eng. Education, pp. 53-58, 2013.

26 Devine & Laingen(2013 midyear)

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28 Ault, H. K. (1999). Using Geometric Constraints to Capture Design Intent. Journal for Geometry and Graphics, Volume 3 No. 1, 39-45.

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Paper ID #9556

A review of the design intent concept in the context of CAD model qualitymetrics

Mr. Jeffrey M. Otey, Texas A&M UniversityProf. Pedro Company P.E., Universitat Jaume I

His research fields of interest are centered on Computer Aided Desig, and Sketch-Based Modeling; withmore than 30 papers and communications published on those areas. Now he is taking part in the devel-opment and applicability of a new sketch-based modeling interface (see http://www.regeo.uji.es/). He hasbeen involved too in Emotional Design and Collaborative Product Engineering. (https://sites.google.com/a/uji.es/pedrocompany/)

Dr. Manuel Contero, Universitat Politecnica de Valencia

Manuel Contero is a full professor of Engineering Graphics and CAD with the Graphic Engineering De-partment at the Universidad Politecnica de Valencia, Spain (UPV). He earned an MSc degree in ElectricalEngineering in 1990, and a PhD in Industrial Engineering in 1995, both from UPV. In 1993 he joinedUniversidad Jaume I of Castellon, Spain (UJI) as assistant professor, promoting to associate professor in1997. In 2000 he returned to UPV, being appointed full professor in 2008. His research interests focus onsketch-based modeling, collaborative engineering, human computer interaction, development of spatialabilities, and technology enhanced learning.

Mr. Jorge Dorribo Camba, Texas A&M University

c©American Society for Engineering Education, 2014

A Review of the Design Intent Concept in the Context of CAD Model Quality Metrics

Abstract From the perspective of Computer Aided Design (CAD), Design Intent is a term commonly defined as a model’s anticipated behavior once it undergoes alteration (ex. will a cylindrical hole continue to share concentricity with a boundary arc should the dimensions be modified?). At present, a standardized manner in which to explicitly communicate or deduce a CAD model’s design intent does not exist. The design tree (feature tree or history tree) in most parametric modeling applications offers implicit depiction of design intent, but not all descriptive information is adequately conveyed (ex. a sketch concentric constraint is not recorded in the design tree and is only accessible if the requisite sketch is opened and examined). An explicit representation would be immensely more valuable, especially for models with complex geometric features or for those working in a collaborative design environment. This paper reviews current understanding of design intent, with an exploration of its relationship to Design Rationale, in the context of product models and their quality enhancement.

Introduction

It is essential for engineers to describe not only the purpose of their designs, but also the justification for specific design decisions. Design Rationale is a term defined as an explicit documentation of the reasons behind decisions made when designing a system or artifact1.

Although design rationale spans a number of diverse disciplines2, it has been a significant issue primarily for software engineering3. However, software design requires different tools and approaches necessary to convey design rationale than those required for product design. Hence, a suitable way to convey design rationale for product design is still essential, a need that can be suitably accommodated with the concept of Design Intent4.

From our point of view, when considering virtual models and assemblies produced by 3D CAD applications, design intent is correlated to anticipated behavior or expected functionality of the artifact undergoing development. It represents what is to be achieved by a design and describes the expected behavior of the model when it undergoes later alteration. To the best of our knowledge, a standardized manner in which to explicitly communicate or deduce a CAD model’s design intent does not exist. Hence, our current research is concerned with defining quality metrics to verify that design intent is properly incorporated in the modeling strategy to construct the CAD model. In this paper, a review of the current understanding of design intent, with its historical connection with design rationale is presented.

Design Rationale

Design rationale is a term that is conventionally understood to describe the purpose of a design, the reasons relating why certain steps were taken in artifact creation, and also aids communication in a collaborative environment, particularly for end users. This process is utilized

in various industries and is often accompanied by graphical structures which help illustrate specific design systems and processes.

Mostow, in investigating the global design progression, stated that design rationale is just one step in the design process5. According to Mostow, design rationale clarifies and justifies why a certain decision was made and why it was thought to be the correct path to take. Design rationales need to be both explicit (clearly defined goals) and appropriate (reasons given why a certain path was chosen). He states that any model of the design process should communicate the state of the design, the goal of the design process, specific design decisions and their justifications, the control of the process, and the role of learning in design. Mostow also claims that previous design rationales can be valuable in solving new problems, particularly when historical decisions and the reasons behind them hold true in future applications.

MacLean et al.1 define the concept of design rationale, highlighting its role as an aid for both designers and end users. They also introduce a “semi-formal notation” aimed at representing design rationale. In a later work, MacLean et al. coined a process titled “Design Space Analysis” in order to characterize design rationale and this analysis was embodied by QOC Notation (Questions, Options, and Criteria) 6. QOC Notation refers to questions identifying design concerns, options providing solutions to the questions, and criteria used to evaluate possible solutions. Design Space Analysis does not provide a written record of the design process, but is considered a co-product of the design and is required to be constructed alongside the artifact. Design Space Analysis supports not only the original design process, but also re-design and reuse by providing an explicit depiction of the process to assist reasoning about the design and the concerns of future alteration. It also provides a method for communication between the designers and system operators.

Lee and Lai placed the emphasis on “tasks” and developed a framework which allowed them to acquire and assess design rationale representations7. This framework increasingly discerns explicit elements of design rationale and supports multiple design tasks. They discuss and evaluate Decision Representation Language (DRL) in order to accomplish these tasks. DRL is used not only to support various design tasks, but can be used to assess and evaluate different design representations.

Henderson, in attempting to integrate physical and conceptual models, divided product models into physical and meta-physical domains8. The physical domain integrates all information related with a model’s actual manifestation, such as geometry, dimensions, and materials while the meta-physical realm refers to information that describes the structure and behavior of the model. It is argued that meta-physical modeling provides the capability to capture the function and design intent of systems, assemblies, parts, features, and even individual dimensions and tolerances. This modeling process uses Product Definition Units (PDU), which are shells in which to encapsulate information. Henderson indirectly defines design rationale, as he defines design intent as "the purpose or underlying rationale behind an object." While this definition does not represent the current understanding of design intent, the term attempts to explain the difference between intent and functionality (“intent justifies a design decision whereas the functionality just tells what the design does”).

Karsenty evaluated the importance of representing design rationale in cases where the original design is reused9. His research questioned six designers about their need to understand previous design rationale, how archived design rationale was used, and how to effectively acquire design rationale. He states that design rationale could be beneficial for those requiring reinforcement for design-based decisions, but it is not adequate to be used as the sole support. In fact, he used the QOC Notation originally developed by MacLean et al.6 in order to document design rationale.

Regli et al. state that design rationale provides an explanation of why an object is designed in a certain manner2. They explain that design rationale encompasses all information generated in product construction (reasoning, trade-offs, etc.) and facilitates communication with personnel who are involved in the artifact, but not in the design phase. An object is defined by its specifications or the way it operates, but often the methodology used to design the object is left unstated. A problem develops when design collaboration is needed and communication is absent. Design rationale is crucial to avoid these problems. They state that the need for design rationale is a collective problem, encountered in all industries, but design rationale systems are uncommon. Design rationale systems need to assess design approaches, representation schema, capture, and retrieval. A system which could capture such information would be important for those tasked with managing design data.

Bracewell et al. examined a Design Rationale Editor (DRed) 10. This software instrument is used to archive decisions and rationale throughout the design process. DRed allows the designer to examine various decisions such as options considered and counterbalancing arguments. It then characterizes these decisions and records them in a graph illustrating various dependencies. They argue that the utility of DRed is based not only on acquiring design rationale, but synthesizing analysis, problem perception, developing solutions, and specific design tasks.

To summarize, Mostow5 first realized the importance of making the design rationale explicit, but his work was aimed at finding better models of the design process. On the other hand, MacLean et al.1 focused on defining and representing design rationale. So they emphasize its importance, describe its benefits, and develop a representation to make it explicit6. Unfortunately, their representation is aimed at computer software design and does not consider product design peculiarities. Lee and Lai7 highlighted the importance of choosing a suitable representation, and provided a framework for evaluating a design rationale representation, but they still were also focused on software design. On the contrary, the work by Henderson8 defines design intent and design rationale for product models even though this definition does not represent the current understanding of design intent. A recent contribution on this context is due to Zhang et al.4, which is important because it not only highlights the relationship between design intent and design rationale, but also investigates why few design rationale systems have been implemented in industry. It appears that limitations exhibited by traditional approaches for capturing design rationale summarized by Karsenty9 and recently addressed by Bracewell et al.10 are still valid. Figure 1 illustrates an IBIS-like schema (Issue-Based Information Systems), created by the authors, summarizing the state of the art for design rationale and design intent. We note that the schema follows the IBIS style (first proposed by Kunz and Rittel in 197011) which is still the base on top of which new schemas are being developed (like ISAA (Integrated Issue, Solution,

Artifact, and Argument) by Zhang et al.4. It can therefore be seen that Henderson contributed to design rationale indirectly. Design rationale encompasses purpose, decisions, and communication. Functionality conveys purpose, and the literature on function reveals that this is a separate ambit where there exist many views of function, and not all of these views are made explicit12. Design intent is also a stand-alone problem, which we will consider in the next section.

Figure 1. Schema illustrating current understanding of design rationale and design intent.

We note that the extent to which we can benefit from design rationale depends largely on the language we use to represent it7. The work by Karsenty9 is a significant contribution on measuring goodness of captured design rationale. The work by Bracewell et al.10 is also noteworthy as it describes a strategy to implement customized tools to capture, represent, and retrieve design rationale.

Finally, apart from an interesting review of the early contributions and the open problems, the work by Regli et al.2 is also interesting as it clearly states the multidisciplinary nature of design rationale and attempts to abstract the place of systems and tools for design rationale capture and retrieval in the context of CAD tools.

Design Intent Definitions and Measurement

Design intent is commonly understood to describe a model’s anticipated behavior once it undergoes alteration. Design intent is such a nebulous concept that applicable standards (ASME Y14.41-2003 and ISO 16792) do not provide a definition of design intent at all. While an official definition of design intent does not exist, many authors have attempted to define the term. In reality, it is a common assumption that a standard definition is understood already, as some

authors use the term without providing any definition: ex. Ault, in her paper on using geometric constraints capture design intent13.

Kimura et al. define design intent as the way original designers articulate the objectives of the design so that the manufacturer can understand the design process in order to ensure proper manufacturability without hampering design performance14. Design intent defined in this manner incorporates design requirements, behavior, and function while facilitating communication between designers and builders. They further state that design intent plays a vital role in communication in simultaneous design.

Rynne and Gaughran, in their research on modeling strategies in CAD pedagogy, define design intent as a description of how an object is modeled and also how it should perform once it is altered 15. They also assert that CAD software records the succession of features used to create a model, which reflects the user’s opinion of the best approach to accomplish a specific task. They further state that design intent should be more comprehensive than shapes and sizes of features, but must encompass consideration of manufacturing methods and relationships between features. A student’s ability to accurately model an object correlates with their ability to visualize and assemble the objects cogently.

Zhang and Luo state that CAD illustrates design intent through its history, features, parameters, and constraints16. They state that design intent not only describes an artifact’s requirements and constraints, but can also serve an expectant role in the design process. Their research examined methods used to share design intent information between models, but encountered difficulties resulting from an absence of standards and data-exchange procedures. Dorribo-Camba and Contero echoed these thoughts by stating that design intent is often embedded in the modeling approach and in the dependencies between features in the CAD software17. Their research details methods to represent annotations in order to enable increased design communication. These annotations are then housed and integrated in a Product Lifecycle Management (PLM) system.

While many authors have comparable definitions of design intent, they each rely on different methods in order to communicate this information to others. Some believe that the parametric modeling software can accurately record this data, but while the software can indeed reflect the specific steps taken to create the artifact, it cannot relate why certain commands were used (ex. why was it considered to be superior to "extrude" a profile rather than to "revolve" a profile?). In the authors' opinion, methods need to be developed so that this information can be documented and design justifications understood, and it would also be highly beneficial if this data extraction could be represented in a graphical format.

Even when commonalities exist between various definitions of design intent, oftentimes the manner in which it is assessed (if it is even assessed at all) is flawed. As just to name one example, design intent that is judged by the amount of features is inherently flawed, as the number of features could be independent of model efficiency.

Design Intent Instruction

There has been abundant research performed into methods to increase the amount of design intent available to be communicated, with much of this effort being aimed at beginning CAD learners. Condoor states that historically, there was one correct depiction of an artifact18. But with CAD, that artifact may be created using several different approaches, with some techniques being superior in that they more successfully reflect design intent. Condoor defines design intent as "the purpose or function of a feature in a part or of a part in an assembly." He determined that there is a substantial connection between the methodology used to create models and the inherent design intent. He proposes a procedure to instruct CAD learners to better reflect design intent by subdividing assemblies into parts, and parts into specific entities; identification of symmetry; proper datum plane orientation; design sequence; and hypothetical changes.

Hartman, in a two-part study attempting to determine how experienced CAD designers achieved their current level of expertise, states that new CAD learners need curriculum that provides instances where models are created, altered, and model geometry can be manipulated so that they can be adequately prepared for real-life design complexity19 and 20. Curricular exercises need to be created so that the correctness and acceptability of an artifact can be related to the model's response to future design changes, both expected and unexpected.

Johnson and Diwakaran state that while rapid model creation is valued, creating designs quickly adversely affected design intent and model perception21. They state that the quality of a model should correlate with the amount of time needed for revision, which attempts in some way to quantify design intent and its communication between users. In a continuation of their research, Diwakaran and Johnson state that CAD models must be easy to change so that design alterations in the product development cycle are accomplished quickly22. It was determined that using simpler features increase the time required to model the original artifact, but increase the reuse of the model in future incarnations. Additionally, simple features, along with use of reference datum and correct feature sequence increase model understanding when undergoing alteration by secondary users. Feature alteration and reuse is positively correlated with model perception.

Li et al. researched methods to detect design intent by primarily using symmetry23. They emphasized identifying design intent by locating prospective geometric abnormalities. Li et al. state that design intent can be properly articulated by geometric constraints and associations between edges, faces, and dependent geometries of CAD models. Their work focused on models bounded by planes, spheres, and cylindrical surfaces, but did not focus on common curved geometries.

Leahy conducted research on methods to encourage best modeling practices on CAD learners in order to ensure proper design intent24. Leahy suggested that well-timed feedback of student performance is needed so that students can incorporate best practices for design intent. He suggests that this feedback be non-graded in order to encourage students to strive for deeper knowledge instead of being motivated only by higher marks.

Company et al. conducted a pilot study and found that instructing beginning CAD users to employ parametric modeling software oftentimes does not include appropriate levels of

instruction on model assessment and evaluation25. They suggest simultaneous introduction of proper modeling strategies when learners are beginning to model, using specific rubrics in order to evaluate a model’s representation of design intent. It was also determined that instruction of proper modeling strategies does not necessarily imply that proper model evaluation techniques were also imparted.

Conclusions and Future Work

As a result of the review of the design rationale subject, we can state that design rationale related to product design is now a well-established subject, which has inherited most of its approaches and strategies from design rationale of software design processes, but which is now becoming a stand-alone subject with particular needs, methods and tools.

One of its main peculiarities is the fact that design intent is a main aspect of design rationale of product design. Design intent is commonly, but not always, understood to describe a model’s anticipated behavior once it undergoes alteration, but the manner in which it is assessed (if it is even assessed at all) is flawed. There is a consensus that modeling tools and strategies greatly influence design intent. There is also agreement in the convenience of introducing design intent through proper modeling strategies when learners are beginning to model. Strategies and approaches aimed at adding design intent into CAD models to enhance their quality, together with metrics aimed at evaluating its efficiency, are now receiving some attention.

Plumed et al. researched methods to determine design intent embedded in 2D sketches26. A drawing can be dissected into its features and analysis of these combinations of features can illuminate design intent. The most common features can then be catalogued and identified. Continuing research will attempt to examine the feasibility of creating algorithms which mimic designers' experience and knowledge to deduce design intent from sketches.

It would appear that rubrics would be an exceedingly useful tool in order to facilitate standardized design intent communication. Goodrich, in her pioneering research on rubrics, defined them as tools for assessment that not only specify important curricular concepts, but gradations between quality levels27. Rubrics are important not only for assessment, but also for communication of expectations.

Of current interest, and a topic of particular focus, is how to define qualities of design intent (and model quality) in such a manner that lends itself to easy assessment. More precise definitions of these terms are vital to any productive research being accomplished. What is envisioned is further development of these concepts in order to construct assessment rubrics to accurately represent comprehensive model quality and design intent depiction, with the goal of standardizing such definitions and assessment strategies. These rubrics must be hierarchical in nature, allowing distinct levels of detail, seamlessly woven into the curriculum allowing for cumulative assessment.

The final product of this research would be development of detailed protocols for learners, so that they could self-assess whether their models achieve expected quality criteria. An advanced goal would be to produce design tools which would check and repair intermediate models with

missing quality criteria. It is our conviction that CAD model quality should not be a correlative goal only to be attempted after basic skills are cultivated, but should be a principal goal from the inauguration of instruction.

References

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2. Regli WC, Hu X, Atwood M, Sun W. A survey of design rationale systems: Approaches, representation, capture and retrieval. Engineering with Computers. 2000;16(3-4): pp. 209-235.

3. Jarczyk AP, Loffler P, Shipmann III F. Design rationale for software engineering: A survey. Proceedings of the 25th Hawaii International Conference on System Sciences. 1992. Vol. 2. pp. 577-586.

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Paper ID #8775

Modifying an Assembly Project to Improve Student Dimensioning Skills

Ms. Norma L Veurink, Michigan Technological University

Norma L. Veurink is a Senior Lecturer in the Engineering Fundamentals Department at Michigan Techno-logical University where she teaches introductory engineering courses and a spatial visualization coursedesigned for engineering students with poor spatial visualization skills. Ms. Veurink manages severalsummer programs that introduce middle and high school students to engineering. She is the Secre-tary/Treasurer for the Engineering Design Graphics Division of ASEE and is also a member of the Amer-ican Society of Civil Engineers and an advisor for Tau Beta Pi. Her research interests include spatialvisualization, engineering education, and first-year programs.

Dr. Gretchen L. Hein, Michigan Technological University

Gretchen Hein is a senior lecturer in Engineering Fundamentals at Michigan Technological University.Her focus is on how students learn and how to make classes more interesting and applicable to the students.She currently teaches first-year engineering courses along with an introductory course in thermodynamicsand fluid mechanics.

c©American Society for Engineering Education, 2014

Modifying an Assembly Project to Improve Student Dimensioning Skills

Abstract

Many first year graphics courses introduce students to solid modeling and technical graphics. At Michigan Technological University, all engineering students take a two to three semester sequence of introductory engineering courses. The last course in the sequence, ENG1102: Engineering Modeling and Design, focuses on solid modeling (3-D CAD), graphical communication, and computer programming. The solid modeling portion of the class exposes the students to sketch-based and feature-based solid modeling and creating engineering drawings and assemblies. The graphical communication portion of the course includes dimensioning, section views, and view selection. To give the students practice in creating CAD assemblies using parts they modeled themselves, student teams complete a project where they either model an object which consists of a few simple components or design an object using simple components such as PVC pipe. Students are given the components of the assembly and calipers to measure them during at least two class periods and are allowed to collect additional measurements at various times outside of class time. The students submit engineering drawings of the fully dimensioned components in the assembly and the part assembly. In the fall of 2012, the assembly project was modified in one section of ENG1102 in an effort to improve students’ dimensioning skills. In this experimental section, the students were given a spring scale to dissect, measure, sketch, and dimension by hand in a single 75-minute class period. The students created solid models of the components from their sketches, and if necessary dimensions were missing from their initial sketches, they submitted a formal request to the instructor to obtain them. In the two control sections of the course, the students were given the same spring scales to model and measure in two 75-minute class periods. Additionally, if dimensions were needed outside of class, they had easy access to the scales. Individuals from each student team in both the control and experimental sections submitted fully dimensioned engineering drawings of two components of the spring scale. In order to assess if the change in the project did improve students’ dimensioning skills, students were asked to complete a survey regarding the project. Responses to the survey and exam questions on dimensioning were compared. This paper will discuss the findings from these analyses. Background Most engineers will create a drawing at some point in their career. For the object to be created, the material, structure and size need to be documented. Traditionally, students have learned how to do this in a “drawing” course. In these courses, students learn how to construct a multi-view drawing, a schematic or a diagram to illustrate the components of their design. Additionally, they add notes and dimensions to describe the materials and size of the object. Most of the time dimensioning is taught based on the modeling package or the textbook being utilized. There are two typical methods of dimensioning. One is based on a universal standard,

like ASME Y14.5-2009, where the rules for dimensioning parts and tolerances are outlined for students and practitioners. This method is primarily used when specifying a specific manufactured part and is not used in the construction industry.1 In the construction industry, dimensioning emphasizes the structural features of the object and typically, tolerancing is not used.2 For both methods, the overall size of the object, along with the size and location of individual features, are important. Universities teach dimensioning of drawings in different courses and with various coverage. In manufacturing engineering programs, students typically learn dimensioning over a course of several courses. At Southwest Texas State University, dimensioning is explicitly covered in two technical classes. This knowledge is applied in additional upper division courses where students create parts and must dimension them such that they can be built. Therefore, the concepts are reviewed and applied in additional courses.3 In a semester-long course at Mercer University School of Engineering, students utilize pencil drawings and progress to computer-aided drawing (CAD) and solid modeling. The purpose is for students to learn the basics of dimensioning and drawing before using an electronic method. Then when they create a CAD drawing they can draw on their experience of hand drawing to create the object and dimension it. The hand drawing and CAD skills are further developed when they begin to learn solid modeling.4 Other institutions have elected to teach dimensioning and drawing in a totally electronic format. Engineering students learn the basics of dimensioning in the first-year engineering program at Michigan Tech. At Michigan Tech, there are several engineering majors: mechanical, civil, environmental, biomedical, geological, electrical, computer, and materials. The majors using the ASME type approach are: mechanical, biomedical and some materials, electrical and computer engineering students. The latter three majors would need dimensioning skills when the component they are designing or constructing must fit in a certain region or cannot exceed a certain size. The structural type of dimensioning is found more often in the civil engineering field, but also in chemical, environmental and geological engineering. Chemical and environmental engineers design process facilities ranging from refineries to water treatment plants. Geological engineers who focus on either reservoir development or mining operations need to dimension the structures involved in their processes or the geo-strata being impacted by their operations. Therefore, the knowledge of how to dimension an object is needed for all engineering majors at Michigan Tech. Dimensioning engineering parts or designs is a difficult topic for engineering students to learn especially when they do not have a class dedicated to drafting. At Michigan Tech, dimensioning is covered in about two lectures. This means that students need to learn the nuances and rules of dimensioning complex parts in a very short period of time. As shown in the text above, most students spend at least one or more semesters learning dimensioning where at Michigan Tech, students have a very limited exposure. Therefore, creative and innovative methods have been developed to assist students in learning this complex topic. One of the more recent developments is described below.

Description of the Study At Michigan Tech, dimensioning is taught in Eng1102: Engineering Modeling and Design, a course taken by all engineering majors. The course covers solid modeling with Siemens NX, dimensioning, sketching of oblique surfaces and section views, and computer programming with MATLAB. Engineering freshmen who enter the university ready to take Calculus I or higher typically take this course their second semester. For students entering Michigan Tech who are not Calculus ready, this is the third of a three-semester sequence of introductory engineering courses, and they typically take the course in the fall semester of their second year. Other students taking the course in the fall semester are students who transferred from other colleges and universities, students who failed one or more of the introductory courses the prior year, and students who transferred into engineering from other curriculums. This paper focuses on a study of dimensioning skills that was performed in the fall 2012 semester. Dimensioning is typically taught over two class periods. Prior to the first class period where dimensioning is discussed, students watch a 12 minute video and complete an online quiz covering the guidelines mentioned in the video. The video covers dimensioning placement guidelines, compares chaining versus baseline dimensioning methods, and explains the avoidance of redundant dimensions. During the first class period, radial, angular, and cylindrical dimensions are introduced. The students are given a general dimensioning procedure and reminded that all necessary dimensions need to be shown, but no more dimensions than those necessary should be given. Two example objects are dimensioned with the instructor leading the dimensioning exercise and reviewing the placement guidelines as they apply to the objects being dimensioned. Students then dimension three simple objects while the course instructor and teaching assistant provide assistance to individuals as needed. Students submit these three problems at the end of the class period. The instructor uses previous years’ submissions as examples of student work and leads the class in critiquing them the second session. The objects are critiqued relative to style, placement, completeness, and necessity of dimensions. Following the critique of the objects, students dimension three additional objects which contain more detail than the first day and are shown the correct way to dimension the objects at the end of the exercise. Approximately 2 to 2.5 hours are spent in class on instruction and dimensioning exercises. Over the course of the next several weeks, three homework assignments and an additional in-class exercise require students to completely dimension a variety of objects. The first homework assignment requires the students to dimension several objects by hand, while the other two homework assignments and an in-class activity focus on learning the drafting application of NX while applying the dimensioning skills learned earlier. The NX-related dimensioning assignments and exercises require students to submit completely dimensioned engineering drawings of a part. One of the homework assignments requiring students to submit a completely dimensioned engineering drawing of an object is the first component of a larger assembly project. In this project, students work in teams to model and assemble a physical object in NX. The purpose of the project is primarily to give students practice in creating solid models of components which need to be used in an assembly. This helps students gain an understanding of the importance of communication with team members as they model the components. In the fall of 2012, this exercise was modified in one of three sections taught by the instructor. Before the project was

introduced, the instructor polled the students to see how many of the students had taken a drafting class in high school. In one section, 26% of the students said they had taken a drafting class in high school, 35% of the students said they did in another section, and 47% had in the third section. Based on this response, the section where 35% of the students reported taking a drafting class in high school was chosen as the section to complete a modified form of the project. In two of the sections, the control group, students were given a spring scale to measure, sketch if desired, and model in NX in two 75 minute class periods. Students were also told they could come to the instructor’s office as needed to get additional measurements. In the experimental section, students were given a spring scale to measure and sketch in one 75 minute class period and were instructed to make the solid models from the completely dimensioned sketches. Students were told they would only have the one class period to gather the necessary dimensions. Students in the experimental section were made aware that this method (restricting the students to model the object from their sketches) was different than how the project was completed in the past. The spring scale contained ten parts: four red plastic pieces, two metal hooks, a white plastic nut, a clear plastic tube, and two springs. Each student submitted engineering drawings of two of the parts. Because some of the parts were much simpler to model than others and because some of the student teams contained three members while others had four members, each student was to model and create an engineering drawing of one of the red pieces of the scale. To determine if the exercise of having students sketch and completely dimension an actual object and then produce a solid model from that sketch improved dimensioning skills, several measures were compared. Student performance on exam dimensioning questions and responses to a survey about the spring scale project were compared between the experimental and control groups. Class composition by major was also compared as some engineering majors may perceive dimensioning skills to be more critical for their discipline than other disciplines and thus may put forth more effort on the dimensioning exercises. The distribution of majors in the experimental and control groups are compared in Table 1 below. The majors are grouped by perceived need for dimensioning skills. Proper dimensioning is critical in the mechanical, civil and biomedical engineering fields, and it is assumed students in these disciplines recognize this. Chemical, environmental, and geological engineers may have less need for good dimensioning skills depending on the career path they choose, so these students may not perceive as strong a need to master the dimensioning concepts. Electrical, computer, and materials science and engineering students likely feel dimensioning is the least critical to their discipline and thus may put forth less effort than the other engineering groups. As can be seen from the table below, the experimental group had a 17% lower percentage than the control group of students to whom dimensioning is perceived to be the most critical. Additionally, there were 10% more students from the majors where the perception of the need of these skills may be the lowest.

Table 1: Study participants by major Experimental Group

n=52 Control Group

n=103 Mechanical, Civil and Biomedical Engineering 46%

n=24 63% n=65

Chemical, Environmental, Geological 19% n=10

13.5% n=14

Electrical, Computer, and Materials Science 23% n=12

12.5% n=13

Engineering Undecided 8% n=4

8% n=8

Other 4% n=2

2% n=3

Total 100% 100% Results of the study For the spring scale assignment, each student was to model and submit fully dimensioned engineering drawings of one of the four red plastic parts of the spring scale. This was so that each student modeled and dimensioned a part of comparable complexity. Besides the red plastic parts, the other parts of the spring scale included a hex nut, a simple tube, and two wire hooks which students in the control group could easily have traced rather than recording the necessary dimensions before modeling the part. The drawings of the red plastic parts were analyzed for missing and redundant dimensions. During this analysis, it was noticed that student models of one the red parts varied significantly, so the dimensioned drawings of that part were excluded from the analysis. Some students did not submit drawings or did not submit drawings of the correct part, so the number of drawings examined was lower than the number of students in the control and experimental groups. Table 2 below shows the result of this analysis. Students in the experimental group missed more necessary dimensions than did the control group, but had fewer redundant dimensions than the control group. Ten to twelve dimensions were required to fully describe the parts, so the number of missing dimensions was significant for both groups. It should also be noted that two of the parts analyzed below consisted of cylindrical endcaps with holes, slots, grooves, pads, and two different sized cylindrical surfaces. Table 2: Missing and redundant dimensions on spring scale part Experimental Group

n=35 Control Group

n=66 Average number of missing dimensions 2.4 1.77 Average number of redundant dimensions 0.63 0.85 The midterm exam in the course occurred in the eighth week (out of fourteen weeks) of the semester and covered solid modeling in NX, sketching section views and oblique surfaces, and dimensioning. One question on the exam asked the students to completely dimension an object when given two of its orthographic views. A total of eleven dimensions were required to completely dimension the exam object. The number of missing and redundant dimensions on the object was compared for the two groups. Table 3 below shows both groups on average missed placing less than two of the eleven required dimensions on the object. The experimental group

Object 1  Object 2

missed an average of 0.25 more of the required dimensions than the control group. Both groups had about the same average number of redundant dimensions. Table 3: Missing and redundant dimensions on midterm exam by group Experimental Group

n=51 Control Group

n=103 Average number of missing dimensions 1.73 1.49 Average number of redundant dimensions 0.51 0.54

To provide an assessment of the students’ retention of the dimensioning skills, the final exam included several multiple choice questions regarding dimension placement rules and two questions related to how many dimensions were required to completely dimension an object. For these two questions, a partially dimensioned object was given, and students were asked in a multiple choice format how many dimensions needed to be added to completely dimension the objects. In the class, students are required to dimension objects completely, such that no assumptions need to be made by someone who is fabricating the object. This means that even though an object appears to be symmetrical, sufficient dimensions must be placed on the object to fully define the object without having to make this assumption. The first object on the exam included a hole that was obviously centered from top to bottom on the part. Thirteen dimensions were required to completely describe the object, and nine dimensions were placed on the partially dimensioned object. The second object was symmetrical and included a raised rectangular surface centered on a rectangular base. The object also had a small edge blend. Thirteen dimensions were required to completely describe the second object, and eight dimensions were placed on the partially dimensioned object. The two exam objects are shown in Figure 1 below.

Figure 1: Partially dimensioned objects on final exam.

Table 4 below compares the success of students in the two groups on these questions. The percent of students correctly identifying the number of missing dimensions was approximately the same for both the control and experimental groups. However, about 10% more of the students in the experimental group identified the number of missing dimensions as one less than the correct number. A lower percentage of students in the experimental group selected an answer that reflected more than the number of missing necessary dimensions than students in the control group. Table 4: Identification of missing dimensions on the final exam by group Experimental Group

n=51 Control Group

n=98 Correctly identified the number of missing dimensions on object one (Selected four missing dimensions as their answer.)

15.7% n=8

18.4% n=18

Identified the number of missing dimensions on object one as one less than those necessary (Selected three missing dimensions as their answer.)

39.2% n=20

29.6% n=29

Correctly identified the number of missing dimensions on object two (Selected five missing dimensions as their answer.)

27.5% n=14

25.5% n=25

Identified the number of missing dimensions on object two as one less than those necessary (Selected four missing dimensions as their answer.)

23.5% n=12

14.3% n=14

Identified the number of missing dimensions on object one as more than those necessary

5.9% n=3

5.1% n=5

Identified the number of missing dimensions on object two as more than those necessary

7.8% n=4

15.3% n=15

At the completion of the spring scale assembly project, students were asked to complete an on-line survey regarding the project. The questions on the survey and possible responses are shown in Table 5. So that a consistent comparison could be made, several of the questions referred specifically to the red parts of the spring scale. Responses were correlated to 5-point scale which is also shown on Table 5. The intent of the survey was to determine how well the students gathered the necessary dimensions of their part, the time required to model and dimension the part, and the students’ overall rating of the assignment.

Table 5: Survey questions and possible responses regarding spring scale assembly project Rating 1 2 3 4 5 How much drafting/drawing experience did you have prior to this class?

More than four

semesters

Three to four semesters

Two semesters

One semester None

How many dimensions from the red part you modeled did you get from a classmate in the same or another section of Eng1102?

More than 5 4-5 2-3 1 0

How many dimensions for the red part you modeled did you get from the learning center?

More than 5 4-5 2-3 1 0

For the red part you modeled, how long did it take you to model the part?

120 or more minutes

90 minutes 60 minutes 30 minutes 15 minutes

For the red part you modeled, how long did it take you to fully dimension the engineering drawing of the part?

60 or more minutes

45 minutes 30 minutes 20 minutes 10 minutes

How would you rate this assignment with respect to your dimensioning skills?

This exercise provided no benefit to my skills

This was not a learning exercise, but provided good practice for my skills

I learned little during the completion of this assignment

I learned some during the completion of this assignment

I learned a lot during the completion of this assignment

How would you rate this assignment with respect to your NX assembly skills?

This exercise provided no benefit to my skills

This was not a learning exercise, but provided good practice for my skills

I learned little during the completion of this assignment

I learned some during the completion of this assignment

I learned a lot during the completion of this assignment

How would you rate this assignment overall?

Very Poor Poor Fair Good Excellent

I recommend this project be completed in future Eng1102 classes.

Disagree Somewhat Disagree

Neutral Somewhat Agree

Agree

Table 6 summarizes the survey responses of the two groups. The students in the experimental group had slightly less prior drafting experience than students in the control group. The questions concerning getting dimensions from another classmate or from the learning center were asked to ascertain if students in the experimental group were largely able to model their objects in NX from their hand drawings or if they found they missed dimensioning features of their objects. Students in the control group had two class periods in which to model their objects and collect necessary dimensions, but due to the fact the students worked in teams of three or four and there were only two computers available per team, not all students in the control group were able to model their parts in the two class periods. They were told the second class period to collect whatever dimensions they needed as no more class time would be spent on the project and it would be easiest to get all needed dimensions by the end of class. From the survey responses, it appeared both the experimental and control groups missed collecting needed dimensions during their allotted class periods at about the same rate. A few of the students in the control group did come to the instructor’s office to collect additional dimensions; so the control group did miss needed dimensions at a slightly higher rate than the experimental group. The students in the experimental group were able to model their red part in NX slightly quicker than the control group, but both groups took about the same amount of time to completely dimension the engineering drawing of their red part. Table 6: Assembly project survey results by group Experimental Group

n=44 Control Group

n=92 How much drafting/drawing experience did you have prior to this class?

3.39 3.26

How many dimensions from the part you modeled did you get from a classmate in the same or another section of Eng1102?

4.59 4.58

How many dimensions for the part you modeled did you get from the learning center?

4.91 4.89

For the part you modeled, how long did it take you to model the part?

3.95 3.79

For the part you modeled, how long did it take you to fully dimension the engineering drawing of the part?

4.27 4.24

How would you rate this assignment with respect to your dimensioning skills?

3.45 3.18

How would you rate this assignment with respect to your NX assembly skills?

3.78 3.45

How would you rate this assignment overall? 3.61 3.35 I recommend this project be completed in future Eng1102 classes.

4.09 3.53

Overall, the students in the experimental group had a more positive outlook on the project than students in the control group. They found the project to be more beneficial to their dimensioning and NX assembly skills. They rated the overall assignment higher than the control group. More students in the experimental group strongly recommended this project be completed in future course offerings. In addition to the questions on the survey shown above, students were asked to choose the most accurate description of how they felt about the project. Table 7 below shows the experimental group found the project to be less challenging, less frustrating, but more interesting

than the control group. The students in the experimental group, but not the control group, were told the project was being conducted in a different manner to determine if the new method resulted in better dimensioning skills. This may have impacted their overall attitude toward the project. Table 7: Student feelings regarding assembly project by group Overall I found this project to be:

Challenging Enjoyable Interesting Unremarkable Frustrating

Experimental group response

18.2% n=8

15.9% n=7

45.5% n=20

6.8% n=3

13.6% n=6

Control group response 23.9% n=22

15.2% n=14

33.7% n=31

9.8% n=9

17.4% n=16

Conclusion The results of this study are not definitive, but suggest the method of having students sketch and record all necessary dimensions of an object and then creating a solid model and fully dimensioned engineering drawing from the sketch may not be effective. Students in the experimental group under-dimensioned the drawing they created using this method and under-dimensioned an object on the midterm exam at a higher rate than those in the control group. However, on the final exam, the experimental group outperformed the control group in determining how many dimensions were missing from a partially dimensioned object. This suggests the project may have increased the long-term retention of dimensioning skills of the experimental group. Overall, the experimental group had a more positive outlook on the assembly project, however, this may have partly been due to the fact that they knew something different was being tried in order to improve their dimensioning skills. A follow-up study where both the experimental and control groups are told the project is being conducted in a different manner to determine if doing it in this manner improves their dimensioning skills would remove this possible bias. Repeating this study by having students measure and then model objects with few curved surfaces may also prove to be more definitive as the spring scale components in the assembly project had several curved surfaces. It is likely more difficult for novices to properly dimension multiple curved features in comparison to dimensioning multiple linear features of an object. References 1Fisher, B. R. (2013) Geometric Dimensioning and Tolerancing Visual Glossary with GD&T At-A-Glance Sheets. Sherwood, OR: Advanced Dimensional Management LLC. 2Gay, D. & Gamebelin, J. (2013). Modeling and Dimensioning of Structures: An Introduction. Hoboken, NJ: Wiley-ISTE. 3Sriraman, V. & DeLeon, J. (1999). Teaching Geometric Dimensioning and Tolerancing in a Manufacturing Program. Journal of Industrial Technology, Vol. 15(3), 1- 6.

4Mahaney, J. (2004). A Drafting Course for Practicing Engineers. Paper presented at ASME 2004 International Mechanical Engineering Congress and Exposition, Anaheim, CA.