whitepaper on 3g reverse modeling by rapidform

7/28/2019 Whitepaper on 3G Reverse Modeling by Rapidform

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Whit e Paper:

Do it Once!

The Value of 3rd Generation, Parametric

Modeling from 3D Scan Data

M Chader 4/18/2008

This paper

was

presented

at

the

SME

conference

Rapid

2008,

and

is

offered

through

the

Society of Manufacturing Engineers. All rights reserved by the author.

(G)



White Paper: 3rd

Generation Parametric Modeling from 3D Scan Data (F) Martin Chader



The Value of 3rd Generation, Parametric Modeling from 3D Scan Data

Abstract This paper discusses the state of the art of reverse engineering. Non‐contact dimensional measurement

tools such as laser digitizers are used widely for inspection in the production environment. However,

industry has also embraced high‐density measurement tools for shape capture tasks during the design

phase, to:

• capture conceptual design models,

• perform competitive analysis,

• document legacy

parts

and

tooling,

• define the spatial constraints into which a new part must fit.

Each of these uses results in the capture of physical shape into a CAD model, often for further

engineering. What has historically been called “reverse engineering” might more correctly have been

called “shape capture”. Beyond the shape or envelope of the scanned item, very little engineering

information was captured in the result. The 2nd generation, shape‐capture process provides the CAD

designer with little more than a shape template. Many hours are spent to make a watertight mesh, then

a NURBS model, which ultimately are only templates which are used briefly, then discarded in the

process of creating the editable Solid or Parametric CAD model.

CAD is the language to describe designs throughout industry. A new third generation of Reverse

Engineering software enables the creation of CAD‐useful, parametric entitles directly from the scan data

(i.e., the point cloud). 3G modeling empowers the CAD designer to reference with large point clouds,

and from them, to construct CAD models using standard CAD tools. The knowledge of how a part is to

be used drives the sequence in which the CAD model is created. The CAD shapes and the sequence in

which they build upon each other yields more than just the shape; the result embodies the engineering

“Design Intent” in the CAD language of the enterprise.

We’ll explore

where

the

second

generation

modeling

techniques

are

well

suited,

and

make

a case

for

the claim that: If the destination for scan data is a CAD model, the 3rd generation point‐processing tools

yield more information, with less error, in a fraction of the time required for previous techniques.

________________________________________________________________________

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Late in the 20th century, the industrial community received a new tool for dimensional measurement:

structured light was employed to measure range and dimension. This technology has been

commercialized into a class of measurement instruments commonly called 3D scanners or digitizers

which collect many discreet range points from the surface of scanned objects. The aggregate of multiple

measurements describes the object’s shape, from which any single dimension can be extracted. This

wealth of

dimensional

data

was

unprecedented,

and

its

adopters

discovered

what

they

had

suspected:

What you don’t know CAN hurt you. However, processing this wealth of data required new methods to

realize its advantages.

Scott Ackerson is an industry luminary, trained metrologist, and founder of a laser measurement

equipment company. Ackerson observed that the speed at which measurements are made has

increased by orders of magnitude, from one dimension (or linear distance) per minute in the industrial

revolution of the 19th century, to the current day where we have the means to capture a full shape

(millions of dimensions) in less than a second. The progress is illustrated in the following timeline.

Figure 1: Progress of Dimensional Measurement (courtesy Scott Ackerson)

More recently, industrial CT or X‐Ray machines are measuring external and internal geometry in

exquisite detail. Of course, these 3D measurement tools facilitate creation of those organic, free flowing

products that consumers demand. But the measurement tools can’t do that alone. Software tools are

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needed to handle this flood of data, and from it, to create useful information. Just as digital cameras

benefit from the “digital dark room” known as Adobe Photoshop®, 3D digitizers needed their own

enabling software, commonly called “Point‐processing” software, accepting data from measurement

devices ranging from single‐dimension tools to full‐coverage measurement instruments.

Point‐Processing

software

tools

are

generally

built

to

fill

one

of

two

needs:

Modeling,

or

dimensional

inspection.

Inspection compares scan data to the CAD model. To confirm dimensional compliance, the data from

the scanned subject is aligned and compared to a digital reference. Often this reference is a CAD model.

Modeling creates a digital model – frequently a CAD model ‐‐ where none exists. A digital model is

created from the scans of a physical part. Modeling can be segmented further, into the following types:

• Visualization models

o Models for display and communication via digital media. For example, visualization

models allow

a museum

to

show

you

its

sculptures

in

3D

via

the

web,

and

a film

maker

to create a CG effect.

• Verbatim replication models

o Models destined for fabrication back into a physical part, for which little or no further

editing is needed to realize the end product. (e.g., medical prosthetics).

• Modeling for Reverse Engineering

o Modeling to capture the engineering design intent (i.e., the form, individual features

and their functional interrelationship), for importation into the Computer Aided Design

(CAD) environment, as a fully‐functional, parametric, solid model. These are often

models supporting industrial manufacturing.

This paper focuses on modeling, and describes the work flow of point‐processing alternatives for

creation of these digital models.

Design Continuum

Let’s

consider

the

design

of

a

consumer

product,

the

Widget

1000.

A

simplified

description

of

the

process to create our new hand‐held widget might go something like this:

The concepts for the form of our Widget 1000 might first appear as a series of sketches offered up by

the industrial designers, from which some few favorite designs are chosen. Then these promising

sketches might be recreated as foam models, painted and shown to focus groups for their opinion.

From here, the successful concept model is given to design engineers who dutifully fire up their CAD

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systems and recreate the form, while installing the functional bits; mechanisms, power supply,

electronics, and so forth. Along the way to final product, tests and iterations are made on both

computer models (CAE, CAT) and physical models (RP models, prototypes, 1st articles, production parts.)

Our Widget 1000 exists in two alternate universes. The physical domain holds parts, tooling, etc.; and

the intangible

digital

realm

includes

scanned

or

computer

graphic

concept

illustrations,

CAD

and

FEA

models and ultimately production documentation, tooling designs, etc. Throughout the design process,

the Widget 1000 is improved by exercising it in both domains, but that is not the end of it. Even after

the design is complete and the Widget 1000 is in production, the physical and digital domain are both

used each time the dimensions of a production part are inspected against the design nominal (the

dimensioned and toleranced CAD model).

MIT calls this transition between the physical and the digital realms the “design continuum”. This

contemporary approach to product development realizes that the entire product life, from

conceptualization through production, is supported by multiple border crossings between the digital

domain and the physical.

Design

Continuum

Digital RealmProduct represented as

•Sketches,

•CAD models, FEA Models,

•Manufacturing Model dimensioned CAD w/

tolerances

Physical Realm

•Design Models

•Prototypes

•1st

Article•Tooling

•Production Parts

measurement

fabrication

Fig. 2, the Design Continuum

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We use different classes of tools, depending on in which direction we make our border crossing. To

create physical parts or tooling from digital models, we use fabrication tools such as 3D printers or CNC

machines. Conversely, if we want to move from a physical part to a digital model, we use dimensional

measurement tools ranging from the ruler to micrometers and calipers and CMMs to 3D Digitizers and

industrial CT machines, to create our 3D digital models.

Consider the early stage in a product’s life; the design stage.

First of all, starting a design with a “blank screen” is a myth. Whether it’s a Widget 1000 or something

else, all products are designed within design constraints, and these constraints are often dimensional.

Whether a hand‐operated surgical instrument, a pump body casting or an aircraft cockpit; in each case

the designer is constrained by dimensions. Here the constraints are, the surgeon’s hand size and shape,

the flange to which the pump will be mounted, and the shape and reach of the pilot, respectively.

Portable CMMs, 3D digitizers and industrial CT machines have become powerful tools to fully describe

the shapes in question. However, these digitizing devices do not have the intelligence to extract and

partition the

overall

shape

into

discreet

functional

components,

and

they

certainly

cannot

describe

the

relationship of the elemental shapes as they were intended in the original design. That requires an

understanding beyond just the shape.

Figure 3: The brake drum and the birthday cake, similar shapes, but dramatically different design intent.

Let’s look at an example. If we look at shapes above, there is little difference between a Brake Drum

and a Birthday Cake. Both are basically larger diameter cylinders with five smaller cylinders on top, and

while they may have the same dimensions, there is no critical spatial relationship between the carefully

placed candles. Conversely, there is a very specific relationship between each of the lug bolts to the

other four lug bolts and to the drum’s principle axis (e.g., all on a common circle, separated by 72

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degrees, etc.). Anyone who has ridden on an out‐of ‐round tire will recognize the criticality of the

placement of the lug bolts around the spindle center, but only because the observer is aware of the

context in which the five cylindrical shapes are used.

This understanding of the relationship of those five lug bolts and the constraints on how they are placed

is an

example

of

“DESIGN INTENT”.

The

knowledge

of

the

context

in

which

the

brake

drum

shape

must

operate (e.g., the wheel must bolt to it, the drum must mount onto, and rotate about the spindle axis)

and the function it will perform (e.g., provide a braking surface for the shoes to rub against, constrain

the wheel, etc.) all mandate that the brake drum be designed a certain way. Understanding this design

intent, and redesigning a functional part the way the original design engineer did, is the essence of

reverse engineering. This is as opposed to simply making a verbatim copy in a new medium; a process

more akin to translating than engineering.

Physical to Digital Model, Two varieties Verbatim vs. Variable; when to use each

So we’ve identified two types of models; a verbatim model and a reverse engineered, parametric CAD

model, also called a solid model. Both the verbatim and the parametric model have an important place.

Verbatim Model The Verbatim model is useful in cases where the model will be treated as a whole (i.e., not

deconstructed), and where further processing (editing) is limited. Examples of places where verbatim

models are appropriate include applications like:

• Medical prosthetics and orthotics, where quality is defined the fit of the shape of the

prosthetic to the anatomy of the patient, and little or no editing of shape data is

required.

• Sculptures, where an artist’s maquette may be enlarged to a full‐sized bronze, but for

which only limited editing is required (e.g., simple edits like shell thickening, scale, etc.).

• Output to any fabrication device (3D printer or CNC machine) if ‐‐ and this is a BIG if –evaluation of the fabricated part will not result in the need for further editing.

As a rule, we can say that if the model will not be edited (beyond simple changes like scale), then

verbatim models are sufficient.

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Parametric Models, true Reverse Engineering If we anticipate any changes to the engineered part from its current form, or if we want to record and

edit the part’s form in CAD, then parametric models are preferred. Parametric models and CAD are

the language of the mechanical engineering profession, used throughout the design and manufacturing

world to describe engineered parts’ shape and tolerances. If any changes will result to an engineered

form, in nearly all cases they will be made in a CAD environment or documented there.

Back to our example, suppose our brake drum undergoes either physical testing or computer‐based

engineering analysis, showing the need for a greater diameter for the lug‐bolt pattern. No reasonable

engineer would edit the scan data, the polygon mesh or the NURBS network. That would be digital

sculpting; and more aesthetic than analytical. Rather the engineer will modify a single parameter in the

CAD model: the diameter of the circle on which the lug bolts are laid‐out.

Even if change

is

not

anticipated,

a case

can

be

made

to

document

the

part

in

the

same

way

that

the

enterprise documents the rest of its engineered assets, maintaining a standardized set of practices for

dealing with these assets vs. developing new workflows for handling a new class of digital model.

So, when selecting which point processing tool, the question to ask is: “Is this scan data to be the basis

of a CAD model?” If the answer is yes, then the means to capture shape AND the design intent should

be considered; a 3rd generation modeler.

Work flow: From Data to Documentation of the Design Intent, Before we look at the workflow to get our CAD model from scan data, let’s define some terms.

• Point cloud: Consider scanning a sphere, the scanner will measure many discreet points that lay

on the surface of the sphere. Each point is described by its X,Y and Z coordinates. Collectively

these points are commonly called a “point cloud”.

• NURBS Curves: A plane crossing through our point cloud’s center will also intersect some of the

points on the surface. If we connect these dots on the plane with a Bezier curve, we have a

circle. Multiple cross sections will result in more circles that show the extents of our sphere.

These curves

are

a data

structure

that

is

light

enough

to

import

into

CAD

and

to

be

used

as

a

shape template for CAD modeling.

• Polygon mesh: If we start with our unconnected point cloud, and connect each point to its

immediate neighbors with straight‐line vertices, we create many flat facets. All together, these

facets create a surface approximating a sphere (imagine a disco ball). Like the point cloud, this

polygon data structure is also heavy and has historically been handled poorly by most CAD and

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CAM programs. However, it is generally compatible with rapid prototyping and FEA software.

• NURBS Surface patch: Finally if – instead of straight lines ‐‐ we use curves along the longitudinal

and latitudinal directions of our point cloud, and lay a curved patch between the curves

following the contour of our points, we have described our sphere with relatively few patches.

This is

a much

lighter

and

more

CAD

‐friendly

description

of

our

sphere,

and

compatible

with

CAM programs. However, once imported into CAD, the NURBS patches are largely uneditable in

an analytic sense.

Point clouds, NURBS curves and patches were the constructs in the minds of the pioneers of point

processing. Next we’ll consider the work flow.

1st Generation Point Processing Software The advent of scan data was felt most profoundly in the automotive design studios. Prior to scanning,

design studios would use CMMs mounted on large tables to collect the cross section of a newly sculpted

clay model of the next Mustang or Eldorado. These cross sections were brough into CAD and surfaced

to create the tooling for the production stampings. As the potential of laser and white light digitizers

became apparent, the means to handle these large data sets became apparent, to create cross sections

or surfaces that the CAD program could use for down‐stream processing.

In 1992, a post graduate from the University of Michigan named Kurt Skifstad introduced his PhD thesis

as a product called “Imageware”. Imageware was the first generation of point‐processing software

tools, and

it

used

a somewhat

arcane

workflow,

but

its

mission

was

to

make

sense

of

the

data

from

3D

digitizers that were coming into the market. Imageware’s work‐flow involves importing a point cloud.

Next, cross‐sections (NURBS Curves) are extracted by dissecting the cloud with a series of planes, then

the curvature of the points on the plane are approximated by a network of Bezier curves. These curves

could be exported into the CAD, or alternatively, NURBS surfaces were created by lofting the curves, and

exported into the recipient CAD programs.

The CAD operator would import either the curve networks or the NURBS surfaces into CAD. However,

as we discussed, these imported curves and patches represented the shape or envelope of a part, but

acted only as templates from which the CAD designer/engineer would remodel the part in CAD, thereby

incorporating the design intent)

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Fig 4: First Generation modeling work flow. Point cloud ‐> X sections ‐> Lofted NURBS Surfaces

This first generation process was far better than anything that had been available previously, and was

reasonably successful in the market, but the process was laborious and required expert knowledge of

the software. As a result, many service bureaus adopted Imageware and hired out their expertise for a

dear price.

Generation 2, Rapid NURBS Surfacing Later in the 1990s digitizers were becoming more popular, but still the tool of specialists and service

bureaus. It was clear that an easier process was desirable, and a second generation of point‐processing

tools arose to meet the need. Companies such as Paraform, Geomagic, and Rapidform created tools to

automate the process of creating CAD useful data. Many did so by radically changing the workflow.

Fig N: 2nd Generation work flow. Point cloud ‐> Polygon Mesh ‐> “shrink wrapped” NURBS Surfaces

The second generation (2G) process starts with the same point cloud. But instead of creating cross‐

sections, a polygon surface is created by connecting the adjacent points with linear vertices (i.e.,

tessellating points). Now our cloud of unconnected points has been converted to a network of small

interconnected planes describing the shape of the scanned surface. It’s not quite as simple as it sounds.

Typically, significant hand work is involved to “clean up” scanning anomalies. Operations like hole‐

filling, smoothing, decimating, defeaturing, etc. are all part of the process the operator must perform to

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make a clean “watertight” polygonal mesh. Without this work to perfect the mesh, the next step of

creating NURBS surfaces will reproduce anomalies, and exhibit other problems rendering them useless.

This perfected polygon mesh format (often exported as an STL model) is adequate for tasks like rapid

prototyping or 3D printing. However, conversion to NURBS is still necessary for many CAM (CNC tool

path generator

programs)

and

for

import

into

CAD,

as

the

mesh

data

set

was

often

heavier

than

the

original point cloud representation. So an additional process is applied whereby a NURBS patch network

is fitted to the surface. Simply put, NURBS surfaces use a quilt of curved sided patches to approximate

the topology of the mesh surface. When generated automatically, these patches look more like a cargo

net stretched over the surface than the orderly feature‐based patches that a CAD designer would make.

But they are quickly created and are imported easily into CAD – again to act as a template for the

ultimate goal; the parametric model.

Some 2nd‐generation tools also support the means to lay out the patch network by hand, yielding

patches that more closely follow the CAD features. But these are still usually free‐form patches (dumb

shapes) rather

than

parametric

features.

Further,

there

is

none

of

the

definition

of

relationships

(e.g.,

all lug bolts on a single circle with a parametrically defined diameter).

This 2G process is not completely open loop, but errors often remained hidden for multiple steps. To

see if the CAD model is within tolerance, the operator must complete many intermediate operations,

and sometimes use several software packages before getting feedback on whether the target tolerance

has been achieved between the scan data and the final CAD model. Since the feedback loop was so

large, the time to detect and correct errors is large as well.

Users observed that this process seemed like a lot of extra work. Why would one go to all the extra

effort to make a clean, watertight Polygon model, then a NURBS surface model, to ultimately just use

them as

templates

and

start

on

a third

CAD

model

–discarding

the

first

two,

and

potentially

introducing

undetected errors along the way? Again, the need was clear for a better way.

Generation 3, Full parametric solid model directly from scan data In retrospect, an obvious question arises: If you need a CAD model, why not make a parametric CAD

model directly from the scan data?

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This question is answered in the third generation (3G) modelers that provide discreet editable CAD

entities from the scan data, and a full description of the sequence in which they are modeled, describing

the relationship of the various entities.

Rapidform XOR® from INUS Technology is an example of a product providing all essential elements of

3G. XOR

has

two

kernels,

one

for

handling

copious

data

(i.e.,

point

clouds,

polygons

and

surfaces):

and

the other, the industry‐standard Parasolid® modeler for creating the CAD model. XOR displays both

data sets on the screen in the same coordinate space providing native CAD features derived directly

from scan data.

As a CAD designer creates the features in sequence, he explicitly establishes the relationship between

various features, like the lug bolts in our brake drum example. These parent/child and sibling

relationships are recorded in the familiar history tree format used in such popular CAD modelers as

Unigraphics NX®, Pro/E®, SolidWorks® and CATIA®. Since nearly all the major CAD applications use a

parametric, history‐based approach, the commands and parent‐child relationships established in the

history tree in XOR are transferred to the CAD application, recreating the entire part model with

modeling history intact. An observer watching the import process is impressed to see each command

executed automatically, sequentially and quickly – as though some invisible CAD wizard was modeling at

break‐neck speed.

This is the time for a Caveat Emptor (let the buyer beware). Any software claiming to be a 3rd

generation modeler will be able to: 1) tolerate noisy, imperfect and incomplete scan data, and not

propagate anomalies like waves or noise into the CAD model (the goal is: imperfect scans in, perfect

CAD models out); and 2) export constrained parametric shapes, the commands AND history tree. This is

the important distinction of 3G, instead of exporting only shapes, 3G modelers export the sequence of

native CAD commands as well as their associated shapes.

To provide the shapes, even in parametric CAD‐like primitives, without the constraints (you want those

lug bolts parallel don’t you?), and the association between the shapes as described in the history tree is

only a partial answer. (You want those lug bolts parallel right? And concentric with a single parametric

radius, right?). Without these constraints and relationships, the CAD designer must still remodel the

part in the recipient CAD environment, falling short of the promise of 3G: a native, fully functional

parametric model for parametric CAD like UG NX, Pro/E and Solidworks©.

The

benefits

of

3

rd

Generation

modeling

are

many,

but

the

most

important

are:

• Time Savings.

o By removing the need to create the mesh and surface models, benchmarks have

shown that 3G modelers create the resultant native CAD model in less than 1/3

of the time required for 2nd generation methods (often much less). That is

intuitively obvious; if you can avoid creating two extra models, you save at least

2/3 of the time of 2G methods.

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• Higher Accuracy:

o 3G modelers support multiple measurement tools. By accommodating

measurement tools in addition to the scanner (e.g., CMM, calipers, pin gage,

etc.), the modeler can provide tighter tolerances for critical features than the

scanner alone

could

hold.

o Feedback of any deviations between the CAD feature and the point data are

quickly revealed using the whisker plots and color maps showing deviations, lack

of tangency, etc.

o Easily correctable. Even if a discrepancy is discovered downstream from when it

was made, the history‐based modeler allows the operator to “roll back” to the

step in question, correct the mistake, and see the correction automatically

propagate through subsequent modeling steps.

• Quick learning curve for CAD users:

o The use of common CAD tools and methods reduces the learning curve for users

familiar with CAD modeling.

• Excellent CAD models from marginal scan data

o 2G processes require perfect mesh models. Not so in 3G. Imperfect, noisy or

incomplete scan data is generally sufficient to interpolate features, obviating

the need to spend time cleaning, and giving excellent results from less than

perfect scan data. Because the feedback is so immediate, any feature modeled

beyond the user‐set tolerances is immediately apparent.

By creating full history‐based, parametric models directly from scan data, and by enabling the user to

immediately describe the interrelationship of the features being modeled, 3G modeling does more than

translate a physical shape into a digital template. 3G modeling is true reverse engineering, resulting in a

parametric solid model that embodies the design intent, is fully functional, editable and compatible with

the enterprise’s databases and processes.

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Conclusion:

In conclusion, scanners and digitizers are enabling technologies for manufacturing, but require point‐

processing software to realize their potential. When used for modeling, digitized data is well served by

second generation tools for verbatim models and appearance models. When scan data of engineered

parts is

destined

to

become

a CAD

model,

3rd

generation

tools

offer

compelling

benefits

of

saved

time

and superior results.

whitepaper on 3g reverse modeling by rapidform

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