Int J Adv Manuf Technol (2005)DOI 10.1007/s00170-004-2084-9

O R I G I N A L A R T I C L E

Nagahanumaiah · B. Ravi · N.P. Mukherjee

An integrated framework for die and mold cost estimationusing design features and tooling parameters

Received: 5 August 2003 / Accepted: 6 January 2004 / Published online: 2 February 2005 Springer-Verlag London Limited 2005

Abstract Tooling is an essential element of near net shapemanufacturing processes such as injection molding and die cast-ing, where it may account for over 25% of the total productcost and development time, especially when order quantity issmall. Development of rapid and low cost tooling, combinedwith a scientific approach to mold cost estimation and control,has therefore become essential. This paper presents an integratedmethodology for die and mold cost estimation, based on the con-cept of cost drivers and cost modifiers. Cost drivers include thegeometric features of cavity and core, handled by analytical costestimation approach to estimate the basic mold cost. Cost mod-ifiers include tooling parameters such as parting line, presenceof side core(s), surface texture, ejector mechanism and die ma-terial, contributing to the total mold cost. The methodology hasbeen implemented and tested using 13 industrial examples. Theaverage deviation was 0.40%. The model is flexible and can beeasily implemented for estimating the cost of a variety of moldsand dies by customizing the cost modifiers using quality functiondeployment approach, which is also described in this paper.

Keywords Cost estimation · Die casting · Injection molding ·Quality function deployment

1 Introduction

Product life cycles today are typically less than half of thosein the 1980s, owing to the frequent entry of new products withmore features into the market. Manufacturing competitiveness is

Nagahanumaiah · N.P. MukherjeeCentral Mechanical Engineering Research Institute,Durgapur, India

B. Ravi (✉)Mechanical Engineering Department,Indian Institute of Technology,Bombay, Powai, Mumbai – 400 0076, IndiaE-mail: bravi@me.iitb.ac.inTel.: +91-22-2576 7510Fax: +91-22-2572 6875

measured in terms of shorter lead-time to market, without sac-rificing quality and cost. One way to reduce the lead-time is byemploying near net shape (NNS) manufacturing processes, suchas injection molding and die casting, which involve fewer stepsto obtain the desired shape. However, the tooling (die or mold),which is an essential element of NNS manufacturing, consumesconsiderable resources in terms of cost, time and expertise.

A typical die casting die or plastic injection mold is madein two halves: moving and fixed, which butt together duringmold filling and move apart during part ejection. The construc-tion of a typical cold chamber pressure die casting die is shownin Fig. 1.

The main functional elements of the die and mold includethe core and cavity, which impart the desired geometry to theincoming melt. These may be manufactured as single blocks orbuilt-up with a number of inserts. The secondary elements in-clude the feeding system, ejection system, side core actuatorsand fasteners. The feeding system comprising of sprue bush,runner, gate and overflow enables the flow of melt from ma-chine nozzle to mold cavity. The ejector mechanism is used forejecting the molded part from the core or cavity. All the aboveelements are housed in a mold base set, comprising of supportblocks, guides and other elements. Part-specific elements, in-cluding core and cavity and feeding system are manufactured ina tool room. Other elements are available as standard accessoriesfrom vendors. Mold assembly and functional trials are conductedby experienced toolmakers in consultation with tool designers.

The tooling industry is presently dominated by Japan, Ger-many, USA, Canada, Korea, Taiwan, China, Malaysia, Singaporeand India. The major users of tooling include automobiles, elec-tronics, consumer goods and electrical equipment sectors. Plasticmolds account for the major share of tooling industry. About60% of tool rooms belong to small and medium scale industriesworldwide [1]. The tooling requirement is over US$ 600 mil-lion per year in India alone, with an annual growth rate of over10% during the last decade. In India, the share of different typesof molds and dies is: plastic molds 33%, sheet metal punchesand dies 31%, die casting dies 13%, jigs & fixtures 13%, andgauges 10% [2].

Fig. 1. Construction of a typical pressure die-casting die

The tooling industry is increasingly facing the pressure to re-duce the time and cost of die and mold development, offer betteraccuracy and surface finish, provide flexibility to accommodatefuture design changes and meet the requirements of shorter pro-duction runs. To meet these requirements, new technologies likehigh speed machining, hardened steel machining, process mod-eling, tooling design automation, concurrent engineering, rapidprototyping and rapid tooling have been applied. For successfuloperations and to maintain the competitive edge, it is necessaryto establish quantitative methods for cost estimation.

Our current research aims at developing a systematic and in-tegrated framework for development of rapid hard tooling (diesand molds) for injection molding and pressure die-casting ap-plications. The necessity of a systematic cost estimation modelfor comparative evaluation of different routes to tooling develop-ment motivated us to review the existing models, presented in thenext section, followed by our proposed methodology.

2 Previous work

There is considerable similarity in cost estimation approachesused for product and tooling as reported in technical literature.These approaches can be classified into five groups: intuitive,analogical, analytical, geometric feature based and parametricbased methods, briefly reviewed here.

In the intuitive method, the accuracy of cost estimation de-pends on the cost appraiser’s experience and interpretations. Theestimation is usually performed in consultation with the tooldesigner. The estimator acquires the wisdom and intuition con-cerning the costs through long association with die and molddevelopment. This method is still in practice in small workshopsand tool rooms.

In the analogical method, the cost of die and mold is esti-mated based on similarity coefficients of previous dies and moldsmanufactured by the firm. In this technique, dies are coded con-sidering factors such as die size, die material, complexity, ejector

and gating mechanism. The appraiser starts by comparing thenew die design with the closest match among all previous de-signs. The basic hypotheses are: similar problems have similarsolutions, and reuse is more practical than problem solving fromscratch [3]. However, this approach, also referred to as casebased reasoning, requires a complete case base and an appro-priate retrieval system, which has not been reported for die andmold cost estimation so far.

In the analytical cost estimation, the entire manufacturingactivity is decomposed into elementary tasks, and each task isassociated with an empirical equation to calculate the manu-facturing cost. For example, a common equation for machiningcost is

Machining cost = (cutting length / feed per minute)

×machine operation cost . (1)

Wilson (quoted in [4, Chap. 6, p. 121]) suggested a mathematicalmodel for incorporating a geometric complexity factor in turningand milling operations, given by:

N ( )∑ diComplexity factor I = log2 , (2)tii=1

where

di = ith dimension of feature

ti = corresponding dimensional tolerance

N = total number of dimensions .

This is explained with the help of an example later.Another method called activity based costing (ABC) involves

applying the analytical method to all steps in manufacturinga given product, to estimate the resources (material, labor and en-ergy) involved in each step. Such a detailed approach for variousprocesses, including casting has been developed by Creese [5]. Intool rooms, this approach is used in the case of dies with complex

cavity geometry. The sources of mold cost can be divided intothree categories: mold base cost, functional elements (core, cav-ity inserts) cost and secondary elements cost. In each category,the time needed to obtain the desired geometry by machining isconsidered as a reference for costing [4]. As can be expected, es-tablishing and validating the costing equation, as well as using itin practice, are cumbersome tasks.

In the feature based method, mold geometric features (cylin-der, slot, hole, rib, etc.) are used as the cost drivers. The diemanufacturing cost is then estimated using either empirical equa-tions or tools such as knowledge-based systems and artificialneural networks. Chen and Liu [6] used the feature recognitionmethod to evaluate a new injection molded product design for itscost effectiveness. They assumed that a product is an aggregationof a set of features and feature relationships. These feature rela-tionships were mapped to convert a part feature into mold relatedcost evaluations. Chin and Wong [7] used decision tables linkedto a knowledge base to estimate injection mold cost.

In the parametric cost estimation, technical, physical or func-tional parameters are used as basis for cost evaluation. Thismethod allows one to proceed from technical values character-izing the product (available with design engineers) to economicdata. Sundaram and Maslekar [8] used regression model ap-proach in injection mold cost estimation. Lowe and Walshe [9]used labor involvement in injection mold making as a reference;mold cost was estimated using linear regression analysis.

To summarize, cost similarity and cost functions (cost fac-tors) are the two sets of methods for estimating the mold cost.

In the first set, similarity between a new mold and a previ-ous mold developed in the tool rooms is used as a reference.Intuitive and analogical methods fall under this category. In thewidely used intuitive method, the cost appraiser may not be ina position to identify all the risk factors and to quantify many ofthem. The analogical method can be successfully used for esti-mating the cost of die bases and other secondary elements wheregrouping is much easier. However, in the case of functional elem-ents (core and cavity), grouping becomes a difficult task as theirgeometry, machining sequence and tolerance greatly vary withproduct design.

In the second set of methods, the dependency between themold cost and its drivers are expressed in mathematical func-tions. Analytical method, activity based costing, feature basedmethod and parametric costing methods falls under this cate-gory. While analytical methods are well established for esti-mating the machining cost of simple parts, they are difficult toapply in die and mold manufacturing because of their geometriccomplexity. Similarly, feature based cost estimation is difficultto apply because the current feature recognition and classifi-cation algorithms cannot handle freeform surfaces present inmost of the dies and molds, and other computational techniqueslike knowledge-based systems, fuzzy logic and artificial neuralnetworks may be required to establish the cost relations. Fur-ther, these techniques may not be able to consider the impactof assembly restrictions, surface finish requirements, mold trialsand other factors. The parametric costing method functions likea black box, by correlating the total cost of mold with a limited

number of design parameters, and it is difficult to justify or ex-plain the results.

Menges and Mohren [10] developed an integrated approachfor injection mold cost estimation, in which similar injectionmolds and structural components of the same kind are groupedtogether and a cost function for each group is determined. Thecost components are grouped into cavity, mold base, basic func-tional elements and special functional elements. Machining costfor cavity and EDM electrodes is driven by machining time andhourly charges adjusted by factors like machining procedure,cavity surface, parting line, surface quality, fixed cores, toler-ances, degree of difficulty and number of cavities. The moldbases are assumed to be standard components. Cost of basicfunctional elements like sprue, runner systems, cooling systemsand ejector systems are estimated on a case to case basis. Thecost of special functional elements like side cores, three-platemold, side cams and unscrewing devices is determined basedon actual expenses. One of the limitations is that the machiningtime estimate based on mean cavity depth may not give accurateresults in case of complex shaped molds that require differentmodes of machining like roughing, finishing and leftover mate-rial machining, due to cutting tool size and geometry constraints,orientations and settings. Secondly, the work does not appear toconsider machining cost for secondary surfaces (particularly incase of built-in type cavities or cores), cost implications of moldmaterial (which directly affects cutting tool selection and ma-chining time), secondary operations on standard mold bases (toaccommodate cavities, side cores and accessories, special ejectormechanisms and hot runners etc.), and some cushion in cost esti-mation to take care of additional work during final machining ofmating parts.

This approach uses more than 15–20 analytical models withan average 5–8 variables, which need to be statistically estab-lished, and offers research opportunities.

In general, all of above approaches give relatively accu-rate estimates only when tool rooms are involved in develop-ing a single type of mold (such as injection molds or pressuredie casting dies). Die and mold manufacturing is still regardedas skill and experience oriented manufacturing, and moreoverit is not repetitive in nature. Thus there is a need to developa generic die and mold cost estimation model that can be eas-ily implemented for different types of molds and complexity,and is also flexible to accommodate the decisions of the costappraiser. We propose a cost model to meet the above require-ments, based on the notion of cost drivers and cost modifiers.Cost drivers depend on geometry and machining time. Cost mod-ifiers depend on complexity, and can be customized using a qual-ity function deployment approach, which is also discussed inthis paper.

3 Framework for die and mold cost estimation

The cost components of a typical injection molded automo-tive part (assuming a die life of 250,000 parts) are given inFig. 2 [11]. It shows that mold cost (41%) has a much larger

( )

Fig. 2. Cost break up of a typical injection molded automotive part [11]

share of total cost and therefore must be estimated accurately.Molds for other applications (pressure die casting, forging,sheet metal tools, etc.) also reflect a similar breakup. The moldcost comprises mold material, mold design and manufacturing.Among these mold-manufacturing costs represents the largestshare and is the focus of our work. The structure of the pro-posed mold cost estimation model is shown in Fig. 3. In thisapproach, all geometric features are mapped to machining fea-tures, which are used as cost drivers and their cost is obtainedby the analytical costing method. Other factors affecting thecomplexity of the die and mold are considered as cost modi-fiers. Hereafter, the term mold will be used to represent both dieand mold.

3.1 Cost drivers: core and cavity features

In feature based design, a part is constructed, edited and ma-nipulated in terms of geometric features (such as hole, slot,rib and boss) with certain spatial and functional relationships.The part features are used for generating mold cavity features;Table 1 shows the feature mapping between part and mold. Themold features are analyzed to identify the geometric dimen-sions, manufacturing processes and relative manufacturing cost.Essentially, the size and shape complexity of mold cavity fea-tures, which in turn influence the selection of the manufacturing

Table 1. Part to tooling feature mapping and relative cost

method, act as cost drivers. The manufacturing methods 1D,2D, etc., represent the simultaneous movement of tool or workpiece with respect to axis X, Y , Z , a, b and c, to get the de-sired geometry. The relative cost for feature manufacturing (ba-sic mold cost) is proposed based on our experience. This isuseful when sufficient mold design and cost data are not avail-able. More precise cost estimation can be assured by integrat-ing analytical costing methods with machined features in laterstages.

The manufacturing cost of mold geometry can be calculatedby Eq. 1 using predetermined machining parameters like feed perminute (S) and machine hour rate. The summation of machiningcost of all features gives the basic mold cost:

n ( )∑ L f ( )Basic mold cost Cf = I f Mf , (3)

Sf =1

where,

L f = Total cutting length of feature ( f = 1 to n)

S = Corresponding feed (mm/min)

Mf = Corresponding machine minute rate (hour rate /60)

I f = Machining complexity factor I

n = number of features .

While calculating the machining complexity factor for costestimation purposes, it is not necessary to consider all dimen-sions of a feature (the process engineer will select the manu-facturing process and corresponding machine considering thegeometry as well as tolerance of primary dimension). The ma-chine hour rate already considers these effects. There are otherfactors like the number of settings, number of tooling and theirsequence, which are again dependent on geometric complexity(number of surfaces and their orientation and special relation-ships). We therefore modified Eq. 1 by introducing a machiningprocess constant “K”. The value of K varies from 0.05 for plainturning to 0.5 for EDM and machine polishing processes.

Thus machining complexity factor of a feature is given by:

diI f = K log2 . (4)ti

Part Round boss Round hole Outside Tapered Square hole Square boss L-shape Straight ribs Inclined BSpline/features Concave boss boss ribs NURBS

Mold Round hole Round pin Convex Tapered Square Square L-shape Grooves/ Inclined BSpline/features cavity hole protrusion cavity cavity channels groove NURBS

Dimen- D x L D x L R x L x W D/d x L L x B x W L x B x W L /l x W L x B x W L /l x W Cutting areasions (D x L)

Mfg Milling/ Turning/ Milling EDM Milling Milling + Milling + Milling + EDM 3D Milling +Process EDM Drilling EDM EDM EDM EDM

Mfg method 1D 1D 2D 2D 2D 2D + 1D 2D + 1D 2D + 1D 2D 3D / 5DRelative Cost 1 1 3 4 2 8 6 4 8 10

Fig. 3. Structure of the proposed die and mold cost estimation model

For example, consider a circular hole feature with diameter20+0.018 mm and depth 16±0.010 mm. In this case, diameter 20is a primary dimension and tolerance 18 µm can be achievedby the reaming operation. Therefore, it becomes necessary to

consider only the depth that is, 16±0.010. Reaming operation isnormally performed in either CNC vertical machining center ora jig-boring machine. The number of settings is one, and thenumber of tooling is four (center drill, pilot drill, final drill and

( )

Table 2. Cost impact of side core com-plexity (γc) Die construction complexity Injection molds Pressure die casting dies

Side core Extra cavity Side cores Extra cavityγc γcv γc γcv

Uncomplicated parts without cores 0 2.5 % 0 5%

Parts with some complexity, often withoutcores or with few cores

3–5% 5.0 % 5–10% 8%

Complex parts, often with one or severalcores that move in the same direction

5–10% 7.5% 10–15% 11%

Very complex parts with cores in severaldirections

10–25% 10% 15–30% 15%

machine reamer). Therefore, the machining process constant isconsidered as 0.2. Hence the machining complexity of the abovefeature is given by:

16I f = 0.2 log2 = 1.93 .

0.020

3.2 Cost modifiers: Die complexity factors

In die and mold manufacturing, there are many die complexityfactors that have a significant impact on the total cost and areconsidered as cost modifiers. These include parting surface com-plexity, presence of side cores, surface finish and texture, ejectormechanism and die material. Their values, established from ourexperience, are given in Tables 2–4, as a percentage of the ba-sic mold cost (derived from Eq. 3). These are explained in detailhere.

3.2.1 Parting surface complexity

Selection of the most appropriate parting surface is an im-portant activity in die and mold design. Many researchershave reported different algorithms to identify a parting sur-face considering the ejection of part from die cavity, ease of

Table 3. Cost impact of surface finishing (γp)

Type of surface finish Cost modifier γp

Surface finish Ra > 0.8 µm 5–10%Surface finish Ra < 0.8 µm 10–18%Surface texturing by EDM 15–25%Surface texturing by etching 20–35%

Table 4. Cost impact of ejector mechanism (γe)

Type of ejectors Cost modifier γe

Round ejector pins / blades 1–5%Stripper plate, sleeve ejections 5%Self screwing mechanism 5–10%Hydraulic-pneumatic ejectors 10–15%

manufacturability and aesthetic issues. A complex parting sig-nificantly increases the manufacturing cost due to increase inmachining complexity (because of cutting tool geometry con-straints) and die assembly time. A non-planar parting surfacemakes it difficult to match the two halves. Often it results inre-machining, which is not quantifiable by feature based ap-proach. To consider these uncertainties, die parting surfacecomplexity is divided into three levels: straight, stepped andfreeform parting surfaces. Straight parting surface will not im-pose any additional cost, however the cost implications ofsteeped and freeform parting surface will 10–20% and 20–40%, respectively. This can also be customized as discussed ina later section.

3.2.2 Presence of side cores

The product geometry may comprise a number of undercuts tothe line of draw, hindering its removal from the die and mold.This is overcome by the use of side cores, which slide in sucha way that they get disengaged from the molded part before itsejection. Side cores need secondary elements like guide ways,cams and hydraulic-pneumatic actuators, which impose an addi-tional cost. If product geometry calls for a number of side coresthat are actuated in different directions, then die size and costwill increase significantly. Aggravated by additional die coolingarrangements, increased mold assembly time and finish machin-ing during assembly, which may not be easily quantifiable. Whilethe cost of side cores machining is already considered in costdrivers, their influence on over all die complexity due to addi-tional accessories, and secondary machining is considered here.The corresponding values for this cost modifier (γc) are given inTable 2 based on our experience.

3.2.3 Surface finish and texture

The die surface is usually polished to obtain surface roughnessRa from 0.2 to 0.8 µm. Some surface textures may be addedto injection-molded parts to increase the aesthetic look or somefunctional requirement. This requires specialized processes likeEDM texturing, photo etching and surface treatment, increasingthe toolmaker’s job. Therefore, polishing and texturing imposeadditional cost, and the values for this cost modifier (γp) arelisted in Table 3 based on our experience.

[ ( )]( )

3.2.4 Ejection mechanism

The mechanism for ejecting a part from its mold or die may com-prise a simple ejector pin or cam operated mechanism, or a com-plex hydraulic-pneumatic actuator. Construction of the ejectormechanism depends on the part geometry and the desired rate ofproduction. In addition, ejector design may lead to a larger diesize to accommodate the sliders, cams, actuators, etc. The ejec-tor materials are usually of special grade, requiring hardeningand nitriding treatments. Therefore, the ejector mechanism addsto the total cost depending on its type. The values for this costmodifier (γe) are given in Table 4.

3.2.5 Die and mold material

The die and mold material should have good mechanical proper-ties like high hardness, low thermal distortion, high compressivestrength and manufacturability. Commonly used tool steels forinjection molds and pressure die casting dies include P20, P18,EN-24, A3, D1, D2, H11 and H13, which are more expensivethan general steels. The die material cost is directly based on thevolume of die inserts (considered in the total cost model). Thedie material also affects the feature manufacturing cost, becauseof its impact on cutting tool life. A recent development is highspeed machining of hardened die steel, which shows significantimprovement in accuracy and surface finish. Based on an aver-age of ten case studies carried out at our center, the die materialfactor (γm) can increase the basic mold cost by 2–10%, for diematerials ranging from carbon steel to hot die steel.

3.3 Total cost model

The total cost model for die or mold manufacturing is determinedby taking the basic feature machining cost and modifying it usingvarious die complexity parameters, then adding the cost of sec-ondary elements and other activities.

Total mold cost = die material cost

+ (basic mold cost × cost modifiers × number of cavities)

+ (Standard mold base cost × assembly factor)

+ secondary element cost + tool design and tryout charges.

n ( )∑ L f Mc = Cm + I f Mf

Sff =1

γps +γc +γp +γe +γm× 1+ nc100γa+Cb 1+ +Cs +Cd (5)

100

where,

Cm = Die material cost

nc = Number of cavities

Cb = Standard mold base cost

Cs = Secondary element cost including ejector, sprue, guides

and screws

Cd = Tool design and tryout cost = 15−25% of total mold

manufacturing cost

γps = Cost modifier due to parting surface complexity

γc = Cost modifier due to side cores

γp = Cost modifiers due to polishing and surface texturing

γe = Cost modifiers due to ejector mechanism

γm = Cost modifiers due to material machining characteristics

γa = Cost modifiers for assembly preparation.

The factor γa includes material handling and additional laborcost, and varies from 5–20% depending on the die size.

4 Establishing the cost modifiers

As seen from Tables 2–4, the impact of various factors on thetotal cost of a die or mold cost is significant. While the valuesgiven in the above tables are based on our experience, they can-not be justified in other tool rooms, unless they have a large casebase to verify the same. The cost modifiers must therefore becustomized for an individual tool room.

One way to customize the cost modifiers is by using multipleregression analysis. This involves collecting historical data andestablishing the regression coefficient or cost estimating rela-tionships (CERs). However, the CERs established in commercialtool rooms may not simulate the real situation, since such toolrooms manufacture a large variety of dies and molds, and a hugeamount of historical data would be required for computation.

We propose another approach, based on quality function de-ployment (QFD) for establishing the cost modifiers, to overcomethe above limitation.

This QFD-based cost model is project specific, and estab-lishes the cost factors by considering the different tooling param-eters. The user has to assess the impact of tooling parameters(parting surface complexity, surface finish, etc.) by consideringbasic mold cost as a reference. This improves the accuracy of totalcost estimation. Table 5 explains the tooling parameters and theirassociated cost factors considered in developing the QFD-basedcost model. The steps involved in the methodology are as follows:

1. Identify major tooling parameters other than basic die andmold feature manufacturing.

2. Categorize the tooling parameters into different complexitylevels (columns of QFD).

3. Identify cost elements other than basic mold manufacturingcost (rows of QFD).

4. Represent the importance of these cost elements in percent-age of basic mold cost. For example, parting surface machin-ing cost is about 10% of basic mold cost, and hence 0.1 isused as cost appraiser’s preference.

5. Develop the relationship matrix considering the complexity,using 1–9 scale (1 = weak, 3 = medium, 9 = strong)

∑∑ ∑

Table 5. Major tooling parameters and associated cost factors

Sr. No. Tooling parameter Cost factors

1 Parting surface complexity Parting surface machining costDie assembly costRe-machining cost

2 Presence of side core Mold housing machining costAccessories preparation costDie assembly cost

3 Surface texture and finish Finish machining / polishing costSurface treatment cost

4 Ejector mechanism Ejector material / std costMachining & assembly charges

5 Die material condition Heat treatment costCutting tool cost

6. Construct the correlation matrix using 0.1–1.0 scale (0.1 =weak, 0.3 = medium, 0.9 = strong)

7. Normalize the relationship matrix using the Wassermanmethod. The coefficient of the normalized matrix is given bythe following equation [12]:

m(ri. j ·γk. j)

rnorm = k=1, (6)i. j m m

(ri. j ·γj.k)j=1 k=1

where

ri. j = coefficient of relationship matrix

γj.k = coefficient of correlation matrix .

8. Calculate the technical importance of each tooling parameter.9. The technical importance values can be used as respective

cost modifiers.

The entire methodology for die and mold cost estimation isillustrated with an industrial example in Sect. 5.

5 Industrial example

Figure 4 shows an aluminum part used in ceiling fans, along withthe corresponding die inserts. The fan component is producedusing cold chamber pressure die casting process. The die design

and development was relatively difficult as the part consists ofa number of small geometric features and split parting surface.A combination of CNC and EDM processes are used to manu-facture core and cavity die inserts in H13 material. Mold bases,ejectors and screws are purchased from standard vendors.

5.1 Basic mold manufacturing cost

A CAD model of the casting was used as input to design the die.To estimate the basic mold cost, the mold machining features andthe corresponding processes were first identified. Then the fea-ture machining cost was estimated using Eq. 3. The feature andits critical dimensions di (ith dimension of feature) and corres-ponding dimensional tolerance ti (dimensional tolerance of ith

dimension) were considered in calculating the complexity factor.The results are shown in Table 6. The following rates were used(in Indian Rupees; 1 INR ≈ US$ 0.02):

Turning operation: Mf = INR 400/hr (CNC lathe)

3D Milling operation: Mf = INR 700/hr

(CNC machining center)

2D milling operation: Mf = INR 120/hr (conventional milling)

EDM operations: Mf = INR 250/hr

Wire cut EDM: Mf = INR 400/hr

Jig boring: Mf = INR 300/hr .

5.2 Cost modifiers

The main complexity characteristics of the die considered in thisexample are as follows:

• Straight parting surface (simple)• Circular cavity split on both sides (chances of mismatching)• 12 ejector pins (diameter minimum 3 mm, maximum 8 mm)• Die material H13 (needs hardening and tempering, hard to

machine)• Surface finish Ra < 0.4 µm (needs polishing)• Number of side cores: Nil• Number of core pins: 12+1 (alignment is critical).

The QFD model was developed as discussed in Sect. 4. Theeight cost elements are represented in the first column of the

Fig. 4. Pressure diecast component and die inserts

Table 6. Basic mold cost (using Eq. 3)

Tooling Mold features Num. of Machining Cutting Complexity Mfgelement (Cost drivers) features method L f /Sf factor (I f ) cost

Cavity Circular cavity (female) 1 CNC Turning 22400/160 1.3 1213Circular hole for core pin 3 Jig boring 3000/100 1.4 630Central hole for core insert 1 CNC Turning 2000/160 1.4 116Gates (feeding +overflow) 7 EDM 0.9/0.01 1.5 984Grove (circular) 1 Turning 1800/100 1.8 216

Core Circular core (male) 1 Turning 25820/120 1.5 2151Central stepped hole 1 Turning 1200/80 1.2 120Ribs 6 CNC Milling 300/60 1.3 455Blind holes 12 Milling 100/60 1.2 280Ribs (small) 12 EDM 3/0.01 1.0 15000Land 1.6 mm depth 6 EDM 1.6/0.01 1.0 4000Ejector pin hole 18 Jig boring (reaming) 100/20 1.4 630Runner 1 CNC Milling 3923/180 1.2 305Overflows pocket 6 Milling 3056/150 1.0 1426

Core pins Circular rods 6 CNC Turning 720/60 1.4 672

Cavity pins Circular rods 6 CNC Turning 745/60 1.4 695

Actual manufacturing cost of functional parts (core, cavity and core pins/inserts) 28893

Miscellaneous operations (blank preparation, reference plane machining, surface grinding) 5778= 20% of actual machining cost

Basic Mold Cost 34671

Note: Cutting length L f for different operations are given by the following:Turning = Length of turning ×number of cutsMilling = Length of feature × (width/step over) number of cutJig boring = Feed length (depth of bore)EDM = depth of pocket to be finishedWire cut EDM = total travel length

QFD model shown in Table 7. The decisions of the cost ap-praiser are represented in the second column, in terms of per-centages of basic mold cost. For example, cost appraiser’s as-sessment for parting plane and associated machining is 10%of basic mold cost; and for housing machining cost to accom-modate functional elements (core and cavity) it is 9%. Thedie design complexity was analyzed and the cost implicationsof the individual parameter were rated using the 1–9 scale tocomplete the relationship matrix. To keep the calculations sim-ple, the correlation matrix was not considered. Table 8 repre-sents the normalized relationship matrix of QFD. The different

Table 7. QFD before normalization

cost modifiers were calculated by adding the coefficients of therespective column.

The impact of various tooling parameters (cost modifiers) ontotal mold cost is given below:

Parting surface factor (γps) = 5.8%

Ejector mechanism factor (γe) = 18.4%

Core pins factor (γc) = 13.6%

Polishing factor (γp) = 14.1%

Die material factor (γm) = 17.8% .

Cost modifier

Cost elements Percentage Straight Ejector Few core Surface Die materialcost w.r.t. parting design pins in finish conditionBasic mold cost surface 12 pins both halves Ra < 0.8

Parting plane machining 0.10 1 3 3 1Re-machining 0.08 3 3 3 9Housing machining 0.09 1 9 3 1Polishing 0.15 1 3 9 9Heat treatment 0.06 9 9 3 9Cutting tool 0.05 1 3 3 9Die assembly 0.10 1 9 3 3Mold trial & rectification 0.07 1 3 3

[ ( )]( )

Table 8. QFD after normalization

Cost modifier

Cost elements Percentage Straight Ejector Few core Surface Die materialcost w.r.t. parting design pins in finish conditionBasic mold cost surface 12 pins both halves Ra < 0.8

Parting plane machining 0.10 0.125 0.375 0.375 0.125Re-machining 0.08 0.166 0.166 0.166 0.500Housing machining 0.09 0.071 0.642 0.214 0.071Polishing 0.15 0.045 0.136 0.409 0.409Heat treatment 0.06 0.3 0.3 0.1 0.3Cutting tool 0.05 0.062 0.187 0.187 0.562Die assembly 0.10 0.062 0.562 0.187 0.187Mold trial & rectification 0.07 0.142 0.428 0.428

Cost importance 0.058 0.184 0.136 0.141 0.178

5.3 Total mold cost

The calculations of total mold cost are given below (in IndianRupees; 1 INR ≈ US$ 0.02):

1. Die material cost: Cm = INR 26325 (approximately 135 kg@ INR 195/kg)

2. Basic mold manufacturing cost = INR 34671 (see Table 6)3. Mold base cost: Cb = INR 58000 (mold base set was pur-

chased from vendors). Assume mold base preparation costγa = 5% of base cost

4. Secondary elements cost = Cs (screws and ejectors) =INR 10200

5. Tool design charge Cd ≈ 15% of basic manufacturing cost =INR (26325+34671+58000+10200)×0.15 = INR 19379.

Therefore, total mold cost using Eq. 5 is given by:

5.8+18.4+13.6+14.1+17.8Mc = 26325+34671 1+

1005+58000 1+ +10200+19379

100= 26325+58836+60900+10200+19376

= INR 175,637 .

6 Validation of the cost model

The cost model developed in this work was validated by usingit for 13 industrial cases, including 7 injection molds, 3 pres-sure die casting dies, 2 wax molds and a compression mold. Allthese were developed at the Central Mechanical Engineering Re-search Institute in India in the last four years. The methodologyfollowed in each cases included:

1. Identification of part features.2. Feature mapping: converting part features into mold features

and then machining features.3. Basic mold cost estimation using Eq. 3.4. Customization of cost modifiers using QFD model as dis-

cussed in Sect. 4.5. Estimation of mold base cost (Cb), secondary elements cost

(Cs) and core and cavity material cost (Cm ).

6. Final die and mold cost estimation using Eq. 5.7. Listing quoted, actual and estimated costs (Table 9). The

quoted cost is based on the tool designer’s experience. Theactual cost is accounted from operator’s machine logbookrecords and manpower schedule. The estimated cost is deter-mined from the cost model.

8. Calculation of deviations for comparative evaluation.

The cost deviations of the two methods, intuitive method(used for quotation purpose) and the proposed cost model, werecalculated and compared (Fig. 5). The average deviation of es-timated cost from actual cost is found to be 0.4% for the pro-posed cost model compared to 2.5% for the intuitive method.The maximum deviations are 2.5% for the proposed model com-pared to 16% for the intuitive method. An additional exercisewas to study the effect of overall complexity of the moldson cost deviation. For this purpose, the examples were sortedin the ascending order of their overall complexity as follows:

Case numbers:

1−2−3−4−11−12−8−9−10−6−5−7−13

Simple −→ Complex

Fig. 5. Cost deviation comparison

Table 9. Results for different case studies (costs in India Rupees)

Type of die / Case Product / die Quoted Actual cost Cost Percentage of deviationmold Description price (accounted) model

estimateQ A E (1− Q/A)∗100 (1− E/A)∗100

Injection 1 4-cavity IM for 50,000 48,300 46,520 −3.51 3.68molds (IM) terminal block 1

2 4-cavity IM for 50,000 46,234 46,400 −8.14 −0.35terminal block 2

3 4-cavity IM for 50,000 53,000 52,700 5.66 0.56terminal block 3

4 2-cavity IM for 50,000 52,800 48,830 5.30 7.51terminal block 4

5 44-cavity IM for 2,00,000 1,93,500 1,82,000 −3.35 5.94cable ties (150I)

6 36-cavity IM for 2,00,000 1,86,000 1,84,650 −7.52 0.72cable ties (200I)

7 Single cavity IM for 2,50,000 2,40,300 2,38,000 −4.03 0.95pump impeller

Pressure die 8 Single cavity PDC die 1,30,000 1,25,000 1,25,500 4.00 −0.4casting (PDC) dies for fan cover type-I

9 Single cavity PDC die 1,35,000 1,28,450 1,32,400 −5.09 −3.07for fan cover type 2

10 Single cavity PDC die 1,70,000 1,74,000 1,75,637 2.29 −0.94for top cover

Wax injection 11 2-cavity wax mold 35,000 33,650 36,200 −4.01 −7.57molds for rear sight

12 Single cavity wax 45,000 46,100 44,890 2.38 2.62mold for bracket

Rubber 13 Split mold for face 2,30,000 1,98,000 2,06,600 −16.16 −4.34compression piece of rubbermold oxygen mask

Mean deviation −2.47 −0.40

It is seen from Fig. 5 that the proposed model gives betterresults than the intuitive method for complex molds, in which ac-curate cost estimations are more important owing to the highercosts involved. The proposed cost model also appears to be moreflexible, and can be easily customized to individual tool roompractices by establishing their own ratings for cost modifiers.

7 Conclusion

Die and mold development procedure varies from part to part andis not very well documented. The conventional cost estimationmethods depend on the experience of the toolmaker and may notyield realistic estimates, especially when die complexity is high.In this work, feature based approach, activity based costing andparametric costing methods were integrated to develop a hybriddie and mold cost estimation model. This cost model is flexibleand project specific, yet easy to apply. A quality function deploy-ment approach has been proposed for customizing the toolingcost modifiers. This enables incorporating the experience of thecost appraiser as well project-specific complexity indicators. Theproposed cost model has been validated on 13 industrial exam-ples, including injection molds and pressure die casting dies. Theaverage deviation was only 0.40% and the maximum deviationwas 7.6%.

The proposed cost model forces a systematic approach,which may be difficult to implement in smaller tool rooms. Sec-ondly, feature identification and complexity rating for customiz-ing the cost modifiers require some expertise and experience.Integrating a computerized database of previous cases, alongwith automated feature recognition can overcome the above lim-itations and also enhance the efficiency of the proposed costmodel. This is presently being investigated.

Acknowledgement The authors would like to acknowledge the Tool andGauge Manufacturers Association (TAGMA), Mumbai, India for sharingthe information on status of Indian die and mold manufacturing industries.The cooperation of the staff of Manufacturing Technology Group, Cen-tral Mechanical Engineering Research Institute Durgapur in die and molddevelopment and establishing the machining process constant is also ac-knowledged.

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