die mould cost estimation

1. An integrated framework for die/mold cost estimation
using design features and tooling parameters
NAGAHANUMAIAH
Scientist, Central Mechanical Engineering Research Institute, Durgapur, India
naga@cmeri.res.in
B. RAVI *
Associate Professor, Mechanical Engineering Department,
Indian Institute of Technology, Bombay, India
bravi@me.iitb.ac.in
N.P. MUKHERJEE
Scientist, Central Mechanical Engineering Research Institute, Durgapur, India
npm@cmeri.res.in
ABSTRACT
Tooling is an essential element of near net shape manufacturing processes such as
injection molding and diecasting, where it may account for over 25% of the total
product cost and development time, especially when order quantity is small.
Development of rapid and low cost tooling, combined with a scientific approach to
mold cost estimation/control, has therefore become essential. This paper presents an
integrated methodology for die and mold cost estimation, based on the concept of cost
drivers and cost modifiers. Cost drivers include the geometric features of cavity and
core, handled by analytical cost estimation approach to estimate the basic mold cost.
Cost modifiers include tooling parameters such as parting line, presence of side core(s),
surface texture, ejector mechanism and die material, contributing to the total mold cost.
The methodology has been implemented and tested using 13 industrial examples. The
average deviation was 0.40%. The model is flexible and can be easily implemented for
estimating the cost of a variety of molds and dies by customizing the cost modifiers
using quality function deployment approach, which is also described in this paper.
Keywords: Die casting, Injection molding, Cost Estimation, Quality Function
Deployment
1. INTRODUCTION
Product life cycles today are typically less than half of those in 1980s, owing to the
frequent entry of new products with more features into the market. Manufacturing
competitiveness is measured in terms of shorter lead-time to market, without sacrificing
quality and cost. One way to reduce the lead-time is by employing near net shape

2. 2
(NNS) manufacturing processes, such as injection molding and diecasting, which
involve fewer steps to obtain the desired shape. However, the tooling (die or mold),
which is an essential element of NNS manufacturing, consumes considerable resources
in terms of cost, time and expertise.
A typical diecasting die or plastic injection mold is made in two halves: moving and
fixed, which butt together during mold filling and move apart during part ejection. The
construction of a typical cold chamber pressure die-casting die is shown in figure 1.
Figure 1: Construction of a typical pressure die-casting die
The main functional elements of the die/mold include the core and cavity, which impart
the desired geometry to the incoming melt. These may be manufactured as single
blocks or built-up with a number of inserts. The secondary elements include the feeding
system, ejection system, side core actuators and fasteners. The feeding system
comprising of sprue bush, runner, gate and overflow enables the flow of melt from
machine nozzle to mold cavity. The ejector mechanism is used for ejecting the molded
part from the core or cavity. All the above elements are housed in a mold base set,
comprising of support blocks, guides and other elements. Part-specific elements,
including core/cavity and feeding system are manufactured in a tool room. Other
elements are available as standard accessories from vendors. Mold assembly and
functional trials are conducted by experienced toolmakers in consultation with tool
designers.
The tooling industry is presently dominated by Japan, Germany, USA, Canada, Korea,
Taiwan, China, Malaysia, Singapore and India. The major users of tooling include
automobiles, electronics, consumer goods and electrical equipment sectors. Plastic
molds account for the major share of tooling industry. About 60% of tool rooms belong

3. 3
to small and medium scale industries worldwide [1]. The tooling requirement is over
US$ 600 million per year in India alone, with an annual growth rate of over 10% during
the last decade. In India, the share of different types of molds and dies is: plastic molds
33%, sheet metal punches and dies 31%, die casting dies 13%, jigs & fixtures 13%, and
gauges 10% [2].
The tooling industry is increasingly facing the pressure to reduce the time and cost of
die/mold development, offer better accuracy and surface finish, provide flexibility to
accommodate future design changes and meet the requirements of shorter production
runs. To meet these requirements, new technologies like high speed machining,
hardened steel machining, process modeling, tooling design automation, concurrent
engineering, rapid prototyping and rapid tooling have been applied. For successful
operations and to maintain the competitive edge, it is necessary to establish quantitative
methods for cost estimation.
Our current research aims at developing a systematic and integrated framework for
development of rapid hard tooling (dies and molds) for injection molding and pressure
die-casting applications. The necessity of a systematic cost estimation model for
comparative evaluation of different routes to tooling development motivated us to
review the existing models, presented in the next section, followed by our proposed
methodology.
2. PREVIOUS WORK
There is considerable similarity in cost estimation approaches used for product and
tooling as reported in technical literature. These approaches can be classified into five
groups: intuitive, analogical, analytical, geometric feature based and parametric based
methods, briefly reviewed here.
In the intuitive method, the accuracy of cost estimation depends on the cost appraiser’s
experience and interpretations. The estimation is usually performed in consultation with
the tool designer. The estimator acquires the wisdom and intuition concerning the costs
through long association with dies and molds development. This method is still in
practice in small workshops and tool rooms.
In analogical method, the cost of die/mold is estimated based on similarity coefficients
of previous dies and molds manufactured by the firm. In this technique, dies are coded
considering factors such as die size, die material, complexity, ejector and gating
mechanism. The appraiser starts by comparing the new die design with the closest
match among all previous designs. The basic hypotheses are: similar problems have
similar solutions, and reuse is more practical than problem solving from scratch [3].
However, this approach, also referred to as Case Based Reasoning, requires a complete
case base and an appropriate retrieval system, which has not been reported for die/mold
cost estimation so far.
In analytical cost estimation, the entire manufacturing activity is decomposed into
elementary tasks, and each task is associated with an empirical equation to calculate the
manufacturing cost. For example, a common equation for machining cost is

4. 4
Machining cost = (cutting length / feed per minute) x machine operation cost – (1)
Wilson suggested a mathematical model for incorporating a geometric complexity
factor in turning and milling operations [quoted in 4], given by:
Complexity factor 2
1
log
N
i
i i
d
I
t=
⎛ ⎞
= ⎜ ⎟
⎝ ⎠
∑ – (2)
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 manufacturing a given product, to estimate the resources
(material, labor and energy) involved in each step. Such a detailed approach for various
processes, including casting has been developed by Creese [5]. In tool rooms, this
approach is used in the case of dies with complex cavity geometry. The sources of mold
cost can be divided into three categories: mold base cost, functional elements (core,
cavity inserts) cost and secondary elements cost. In each category, the time needed to
obtain the desired geometry by machining is considered as a reference for costing [4].
As can be expected, establishing and validating the costing equation, as well as using it
in practice, are cumbersome tasks.
In feature-based method, mold geometric features (cylinder, slot, hole, rib, etc.) are
used as the cost drivers. The die manufacturing cost is then estimated using either
empirical equations or tools such as knowledge-based systems and artificial neural
networks. Chen and Liu used feature recognition method to evaluate a new injection
molded product design for its cost effectiveness [6]. They assumed that a product is an
aggregation of a set of features and feature relationships. These feature relationships
were mapped to convert a part feature into mold related cost evaluations. Chin and
Wong used decision tables linked to a knowledge base to estimate injection mold cost
[7].
In parametric cost estimation, technical, physical or functional parameters are used as
basis for cost evaluation. This method allows one to proceed from technical values
characterizing the product (available with design engineers) to economic data.
Sundaram and Maslekar used regression model approach in injection mold cost
estimation [8]. Lowe and Walshe used labor involvement in injection mold making as a
reference; mold cost was estimated using linear regression analysis [9].
To summarize, cost similarity and cost functions (cost factors) are the two sets of
methods for estimating the mold cost.
In the first set, similarity between a new mold and a previous mold developed in the
tool rooms is used as a reference. Intuitive and analogical methods falls under this

5. 5
category. In the widely used intuitive method, cost appraiser may not be in a position
to identify all the risk factors and to quantify many of them. The analogical method can
be successfully used for estimating the cost of die bases and other secondary elements
where grouping is much easier. However, in the case of functional elements (core and
cavity), grouping becomes a difficult task as their geometry, machining sequence and
tolerance greatly vary with product design.
In the second set of methods, the dependency between the mold cost and its drivers are
expressed in mathematical functions. Analytical method, activity based costing, feature
based method and parametric costing methods falls under this category. While
analytical methods are well established for estimating the machining cost of simple
parts, they are difficult to apply in die and mold manufacturing because of their
geometric complexity. Similarly, feature based cost estimation is difficult to apply
because the current feature recognition and classification algorithms cannot handle
freeform surfaces present in most of the dies and molds, and other computational
techniques like knowledge-based systems, fuzzy logic and artificial neural networks
may be required to establish the cost relations. Further, these techniques may not be
able to consider the impact of assembly restrictions, surface finish requirements, mold
trials and other factors. The parametric costing method functions like a black box, by
correlating the total cost of mold with a limited number of design parameters, and it is
difficult to justify or explain the results.
Menges and Mohren developed an integrated approach for injection mold cost
estimation, in which similar injection molds and structural components of the same
kind are grouped together and a cost function for each group is determined [10]. The
cost components are grouped into cavity, mold base, basic functional elements and
special functional elements. Machining cost for cavity and EDM electrodes is driven by
machining time and hourly charges adjusted by factors like machining procedure,
cavity surface, parting line, surface quality, fixed cores, tolerances, degree of difficulty
and number of cavities. The mold bases are assumed to be standard components. Cost
of basic functional elements like sprue, runner systems, cooling systems and ejector
systems are estimated on a case to case basis. The cost of special functional elements
like side cores, three-plate mold, side cams and unscrewing devices is determined based
on actual expenses. One of the limitations is that the machining time estimate based on
mean cavity depth may not give accurate results in case of complex shaped molds that
require different modes of machining like roughing, finishing and leftover material
machining, due to cutting tool size and geometry constraints, orientations and settings.
Secondly, the work does not appear to consider machining cost for secondary surfaces
(particularly in case of built-in type cavities or cores), cost implications of mold
material (which directly affects cutting tool selection and machining time), secondary
operations on standard mold bases (to accommodate cavities, side cores and
accessories, special ejector mechanisms and hot runners etc.), and some cushion in cost
estimation to take care of additional work during final machining of mating parts.
This approach uses more than 15-20 analytical models with an average 5-8 variables,
which need to be statistically established, and offers research opportunities.
In general, all of above approaches give relatively accurate estimates only when tool
rooms are involved in developing a single type of mold (such as injection molds or

6. 6
pressure die casting dies). Die and mold manufacturing is still regarded as skill and
experience oriented manufacturing, and moreover it is not repetitive in nature. Thus
there is a need to develop a generic die/mold cost estimation model that can be easily
implemented for different types of molds and complexity, and is also flexible to
accommodate the decisions of the cost appraiser. We propose a cost model to meet the
above requirements, based on the notion of cost drivers and cost modifiers. Cost drivers
depend on geometry and machining time. Cost modifiers are depend on complexity,
and can be customized using a Quality Function Deployment approach, which is also
discussed in this paper.
3. FRAMEWORK FOR DIE/MOLD COST ESTIMATION
The cost components of a typical injection molded automotive part (assuming a die life
of 250,000 parts) are given in figure 2 [11]. It shows that mold cost (41%) has a much
larger 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 mold cost comprises mold material, mold design and manufacturing.
Among these mold-manufacturing cost represents the largest share and is the focus of
our work. The structure of proposed mold cost estimation model is shown in figure 3.
In this approach, all geometric features are mapped to machining features, which are
used as cost drivers and their cost is obtained by analytical costing method. Other
factors affecting the complexity of the die/mold are considered as cost modifiers.
Hereafter, the term mold will be used to represent both die and mold.
Cost components of typical injection molded
automotive example
Mold Design &
Simulation
10%
Mold Steel
5%
Mold
Manufacturing
30%Other items
5%
Plastic Material
25%
Injection
Molding
25%
Figure 2: Cost break-up of a typical injection molded automotive part [11]gure 2

7. 7
Figure 3: Structure of proposed die/mold cost estimation model
3.1. Cost Drivers: Core and Cavity Features
In feature-based design, a part is constructed, edited and manipulated 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. The mold features are analyzed to
identify the geometric dimensions, manufacturing processes and relative manufacturing

8. 8
cost. Essentially, the size and shape complexity of mold cavity features, which in turn
influence the selection of the manufacturing method, act as cost drivers. The
manufacturing methods 1D, 2D, etc. represent the simultaneous movement of tool or
work piece with respect to axis X, Y, Z, a, b and c, to get the desired geometry. The
relative cost for feature manufacturing (basic mold cost) is proposed based on our
experience. This is useful when sufficient mold design and cost data are not available.
More precise cost estimation can be assured by integrating analytical costing methods
with machined features in later stages.
Table1: Part to tooling feature mapping and relative cost
The manufacturing cost of mold geometry can be calculated by equation (1) using
predetermined machining parameters like feed per minute (S) and machine hour rate.
The summation of machining cost of all features gives the basic mold cost.
Basic mold cost = ( )f
f
n
f
ff M
S
L
IC ⎟⎟
⎠
⎞
⎜⎜
⎝
⎛
= ∑=1
– (3)
where,
Lf = Total cutting length of feature (f=1 to n)
S = Corresponding feed (mm/min)
Mf = Corresponding machine minute rate ( hour rate/60)
If = Machining complexity factor I
n = number of features
While calculating the machining complexity factor for cost estimation purpose, it is not
necessary to consider all dimensions of a feature (process engineer will select the
manufacturing process and corresponding machine considering the geometry as well as
tolerance of primary dimension). The machine hour rate already considers these effects.
There are other factors like the number of settings, number of tooling and their
Part
featur
es
Roun
d boss
Round
hole
Outsid
e
Concav
e
Taper
ed
boss
Square
hole
Squar
e boss
L-
shape
boss
Straight
ribs
Incline
d ribs
BSpline/
NURBS
Mold
featur
es
Roun
d hole
Round
pin
Conve
x
cavity
Taper
ed
hole
Square
protrusi
on
Squar
e
cavity
L-
shape
cavity
Groove
s/
channel
s
Incline
d
groove
BSpline
/
NURBS
Dime
n-
sions
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
(D x L)
L /l x
W
Cutting
area
Mfg.
Proce
ss
Millin
g/
EDM
Turnin
g/
Drillin
g
Milling EDM Milling Millin
g +
EDM
Millin
g +
EDM
Milling
+ EDM
EDM 3D
Milling
+ EDM
Mfg.
Meth
od
1D 1D 2D 2D 2D 2D +
1D
2D +
1D
2D +
1D
2D 3D / 5D
Relati
ve
Cost
1 1 3 4 2 8 6 4 8 10

9. 9
sequence, which are again dependent on geometric complexity (number of surfaces and
their orientation and special relationships). We therefore modified equation 1 by
introducing a machining process constant ‘K’. The value of K varies from 0.05 for
plain turning to 0.5 for EDM and machine polishing processes.
Thus machining complexity factor of a feature is given by
2log i
f
i
d
I K
t
⎛ ⎞
= ⎜ ⎟
⎝ ⎠
– (4)
For example, consider a circular hole feature with diameter 20+0.018
mm and depth
16±0.010
mm. In this case, diameter 20 is a primary dimension and tolerance 18μm can
be achieved by reaming operation. Therefore, it becomes necessary to consider only the
depth that is, 16±0.010
. Reaming operation is normally performed in either CNC vertical
machining center or a jig-boring machine. The number of settings is one, and the
number of tooling is four (center drill, pilot drill, final drill and machine reamer).
Therefore, the machining process constant is considered as 0.2. Hence the machining
complexity of the above feature is given by:
2
16
0.2
0.020
fI log
⎛ ⎞
= ⎜ ⎟
⎝ ⎠
= 1.93
3.2. Cost Modifiers: Die Complexity Factors
In die and mold manufacturing, there are many die complexity factors that have a
significant impact on the total cost and are considered as cost modifiers. These include
parting surface complexity, presence of side cores, surface finish/texture, ejector
mechanism and die material. Their values, established from our experience, are given
in tables 2-4, as a percentage of the basic mold cost (derived from equation 3). These
are explained in detail here.
3.2.1. Parting surface complexity
Selection of the most appropriate parting surface is an important activity in die and
mold design. Many researchers have reported different algorithms to identify a parting
surface considering the ejection of part from die cavity, ease of manufacturability and
aesthetic issues. A complex parting significantly increases the manufacturing cost due
to increase in machining complexity (because of cutting tool geometry constraints) and
die assembly time. A non-planar parting surface makes it difficult to match the two
halves. Often it results in re-machining, which is not quantifiable by feature-based
approach. To consider these uncertainties, die parting surface complexity is divided
into three levels: straight, stepped and freeform parting surfaces. Straight parting
surface will not impose any additional cost, however the cost implications of steeped
and freeform parting surface will 10-20% and 20-40% respectively. This can also be
customized as discussed in a later section.
3.2.2. Presence of side cores
The product geometry may comprise a number of undercuts to the line of draw,
hindering its removal from the die/mold. This is overcome by the use of side cores,

10. 10
which slide in such a way that they get disengaged from the molded part before its
ejection. Side cores need secondary elements like guide ways, cams and
hydraulic/pneumatic actuators, which impose an additional cost. If product geometry
calls for a number of side cores that are actuated in different directions, then die size
and cost will increase significantly. Aggravated by additional die cooling arrangements,
increased mold assembly time and finish machining during assembly, which may not
be easily quantifiable. While the cost of side cores machining is already considered in
cost drivers, their influence on over all die complexity due to additional accessories,
and secondary machining is considered here. The corresponding values for this cost
modifier (γc) are given in table 2 based on our experience.
Injection molds Pressure die casting diesDie construction complexity
Side core
γc
Extra
cavity γcv
Side cores
γc
Extra cavity
γcv
Uncomplicated parts without cores 0 2.5 % 0 5%
Parts with some complexity, often
without cores or with few cores
3 - 5% 5.0 % 5-10% 8%
Complex parts, often with one or several
cores that move in the same direction
5 –10% 7.5% 10- 15% 11%
Very complex parts with cores in several
directions
10-25% 10% 15 – 30% 15%
Table 2: Cost impact of side core complexity (γc)
3.2.3. Surface finish / texture
The die surface is usually polished to obtain surface roughness Ra from 0.2 to 0.8 μm.
Some surface textures may be added to injection-molded parts to increase the aesthetic
look or some functional requirement. This requires specialized processes like EDM
texturing, photo etching and surface treatment, increasing the toolmaker’s job.
Therefore, polishing and texturing impose additional cost, and the values for this cost
modifier (γp) are listed in table 3 based on our experience.
Type of surface finish Cost modifier
γp
Surface finish Ra> 0.8 µm 5 -10%
Surface finish Ra < 0.8µm 10-18%
Surface texturing by EDM 15 – 25%
Surface texturing by etching 20 –35%
Table 3: Cost impact of surface finishing (γp)
3.2.4. Ejection mechanism
The mechanism for ejecting a part from its mold or die may comprise a simple ejector
pin or cam operated mechanism, or a complex hydraulic/pneumatic actuator.
Construction of the ejector mechanism depends on the part geometry and the desired
rate of production. In addition, ejector design may lead to a larger die size to
accommodate the sliders, cams, actuators, etc. The ejector materials are usually of
special grade, requiring hardening and nitriding treatments. Therefore, the ejector

11. 11
mechanism adds to the total cost depending on its type. The values for this cost
modifier (γe) are given in table 4.
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%
Table 4: Cost impact of ejector mechanism (γe)
3.2.5. Die / mold material
The die/mold material should have good mechanical properties like high hardness, low
thermal distortion, high compressive strength and manufacturability. Commonly used
tool steels for injection molds and pressure die casting dies include P20, P18, EN-24,
A3, D1, D2, H11 and H13, which are more expensive than general steels. The die
material cost is directly based on the volume of die inserts (considered in the total cost
model). The die material also affects the feature manufacturing cost, because of its
impact on cutting tool life. A recent development is high speed machining of hardened
die steel, which shows significant improvement in accuracy and surface finish. Based
on an average of ten case studies carried out at our center, the die material factor (γm)
can increase the basic mold cost by 2-10%, for die materials ranging from carbon steel
to hot die steel.
3.3. Total Cost Model
The total cost model for die or mold manufacturing is determined by taking the basic
feature machining cost and modifying it using various die complexity parameters, then
adding the cost of secondary elements and other activities.
Total mold cost = die material cost
+ (basic mold cost x cost modifiers x number of cavities)
+ (Standard mold base cost x assembly factor)
+ secondary element cost + tool design and tryout charges.
ds
a
bc
mepcps
n
f
f
f
f
fmc CCCnM
S
L
ICM ++⎟
⎠
⎞
⎜
⎝
⎛
++
⎥
⎥
⎦
⎤
⎢
⎢
⎣
⎡
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛ ++++
+
⎥
⎥
⎦
⎤
⎢
⎢
⎣
⎡
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
+= ∑= 100
1
100
1
1
γγγγγγ
– (5)
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 parting surface complexity
γc = Cost modifier due to side cores

12. 12
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 labor cost, 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 the total cost of a die or mold
cost is significant. While the values given in the above tables are based on our
experience, they cannot be justified in other tool rooms, unless they have a large case
base to verify the same. The cost modifiers must therefore be customized for an
individual tool room.
One way to customize the cost modifiers is by using multiple regression analysis. This
involves collecting historical data and establishing the regression coefficient or cost
estimating relationships (CERs). However, the CERs established in commercial tool
rooms may not simulate the real situation, since such tool rooms manufacture a large
variety of dies and molds, and a huge amount of historical data would be required for
computation.
We propose another approach, based on Quality Function Deployment (QFD) for
establishing the cost modifiers, to overcome the above limitation.
This QFD-based cost model is project specific, and establishes the cost factors by
considering the different tooling parameters. The user has to assess the impact of
tooling parameters (parting surface complexity, surface finish, etc.) by considering
basic mold cost as a reference. This improves the accuracy of total cost estimation.
Table 5 explains the tooling parameters and their associated cost factors considered in
developing the QFD-based cost model. The steps involved in the methodology are as
follows.
1. Identify major tooling parameters other than basic die/mold feature manufacturing.
2. Categorize the tooling parameters into different complexity levels (columns of
QFD).
3. Identify cost elements other than basic mold manufacturing cost (rows of QFD).
4. Represent the importance of these cost elements in percentage of basic mold cost.
For example, parting surface machining cost is about 10% of basic mold cost, and
hence 0.1 is used as cost appraiser’s preference.
5. Develop the relationship matrix considering the complexity, using 1-9 scale
(1=weak, 3=medium, 9=strong)
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 Wasserman method. The coefficient of
normalized matrix is given by the following equation [12].

13. 13
∑∑
∑
= =
=
= m
j
m
k
kjji
m
k
jkji
norm
ji
r
r
r
1 1
..
1
..
.
).(
).(
γ
γ
– (6)
where,
ri.j = co-efficient of relationship matrix
γj.k = co-efficient of correlation matrix
8. Calculate the technical importance of each tooling parameter.
9. The technical importance values can be used as respective cost modifiers.
Sr. No. Tooling parameter Cost factors
Parting surface machining cost
Die assembly cost
1 Parting surface complexity
Re-machining cost
Mold housing machining cost
Accessories preparation cost
2 Presence of side core
Die assembly cost
Finish machining / polishing cost3 Surface texture / finish
Surface treatment cost
Ejector material /std cost4 Ejector mechanism
Machining & assembly charges
Heat treatment cost5 Die material condition
Cutting tool cost
Table 5: Major tooling parameters and associated cost factors
The entire methodology for die and mold cost estimation is illustrated with an industrial
example in the following section.
5. INDUSTRIAL EXAMPLE
Figure 4 shows an aluminum part used in ceiling fans, along with the corresponding die
inserts. The fan component is produced using cold chamber pressure die casting
process. The die design and development was relatively difficult as part consists of a
number of small geometric features and split parting surface. A combination of CNC
and EDM processes are used to manufacture core and cavity die inserts in H13
material. Mold bases, ejectors and screws are purchased from standard vendors.
Figure 4: Pressure diecast component and die inserts

14. 14
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 and the corresponding processes were first
identified. Then the feature machining cost was estimated using equation 3. The feature
and its critical dimensions di (ith
dimension of feature) and corresponding 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
Tooling
element
Mold features
(Cost drivers)
Num. of
features
Machining
method
Cutting
Lf / Sf
Complexity
factor (If)
Mfg
cost
Circular cavity (female) 1 CNC Turning 22400/160 1.3 1213
Circular hole for core pin 3 Jig boring 3000/100 1.4 630
Central hole for core insert 1 CNC Turning 2000/160 1.4 116
Gates (feeding + overflow) 7 EDM 0.9/0.01 1.5 984
Cavity
Grove (circular) 1 Turning 1800/100 1.8 216
Circular core (male) 1 Turning 25820/120 1.5 2151
Central stepped hole 1 Turning 1200/80 1.2 120
Ribs 6 CNC Milling 300/60 1.3 455
Blind holes 12 Milling 100/60 1.2 280
Ribs (small) 12 EDM 3/0.01 1.0 15000
Land 1.6 mm depth 6 EDM 1.6/0.01 1.0 4000
Ejector pin hole 18 Jig boring
(reaming)
100/20 1.4 630
Runner 1 CNC Milling 3923/180 1.2 305
Core
Overflows 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)
= 20% of actual machining cost
5778
Basic Mold Cost 34671
Table 6: Basic Mold Cost (using equation 3)
Note: Cutting length ‘Lf’ for different operations are given by the following:
Turning = Length of turning x number of cuts
Milling = Length of feature x (width/step over) number of cut
Jig boring = Feed length (depth of bore)
EDM = depth of pocket to be finished
Wire cut EDM = total travel length

15. 15
5.2. Cost Modifiers
The main complexity characteristics of the die considered in this example 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 Quality Function Deployment model was developed as discussed in section 4. The
eight cost elements are represented in the first column of QFD model shown in table 7.
The decisions of the cost appraiser are represented in the second column, in terms of
percentages of basic mold cost. For example, cost appraiser’s assessment for parting
plane and associated machining is 10% of basic mold cost; and for housing machining
cost to accommodate functional elements (core and cavity) it is 9%. The die design
complexity was analyzed and the cost implications of individual parameter were rated
using the 1-9 scale to complete the relationship matrix. To keep the calculations simple,
the correlation matrix was not considered. Table 8 represents the normalized
relationship matrix of QFD. The different cost modifiers were calculated by adding the
coefficients of respective column.
Cost modifier
Cost elements
Percentagecost
w.r.t.Basic
moldcost
Straightparting
surface
Ejectordesign
12pins
Fewcorepins
inbothhalves
Surfacefinish
Ra<0.8
Diematerial
condition
Parting plane machining 0.10 1 3 3 1
Re-machining 0.08 3 3 3 9
Housing machining 0.09 1 9 3 1
Polishing 0.15 1 3 9 9
Heat treatment 0.06 9 9 3 9
Cutting tool 0.05 1 3 3 9
Die assembly 0.10 1 9 3 3
Mold trial &rectification 0.07 1 3 3
Table 7: QFD before normalization

16. 16
Cost modifier
Cost elements
Percentagecost
w.r.t.Basic
moldcost
Straightparting
surface
Ejectordesign
12pins
Fewcorepins
inbothhalves
Surfacefinish
Ra<0.8
Diematerial
condition
Parting plane machining 0.10 0.125 0.375 0.375 0.125
Re-machining 0.08 0.166 0.166 0.166 0.500
Housing machining 0.09 0.071 0.642 0.214 0.071
Polishing 0.15 0.045 0.136 0.409 0.409
Heat treatment 0.06 0.3 0.3 0.1 0.3
Cutting tool 0.05 0.062 0.187 0.187 0.562
Die assembly 0.10 0.062 0.562 0.187 0.187
Mold trial &rectification 0.07 0.142 0.428 0.428
Cost importance 0.058 0.184 0.136 0.141 0.178
Table 8: QFD after normalization
The impact of various tooling parameters (cost modifiers) on total 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 %
5.3.Total Mold Cost
The calculations of total mold cost are given below (in Indian Rupees; 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 (Table 6)
3. Mold base cost: Cb = INR 58000 (mold base set was purchased from vendors)
Assume mold base preparation cost γa = 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) x 0.15 = INR 19379
Therefore, total mold cost using equation (5) is given by
1937910200
100
5
158000
100
8.171.146.134.188.5
13467126325 ++⎟
⎠
⎞
⎜
⎝
⎛
++⎥
⎦
⎤
⎢
⎣
⎡
⎟
⎠
⎞
⎜
⎝
⎛ ++++
++=cM
= 26325 + 58836 + 60900 + 10200+19376 = INR 175,637

17. 17
6. VALIDATION OF THE COST MODEL
The cost model developed in this work was validated by using it for 13 industrial cases,
including 7 injection molds, 3 pressure diecasting dies, 2 wax molds and a compression
mold. All these were developed at Central Mechanical Engineering Research Institute
in the last four years. The methodology followed 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 equation 3.
4. Customization of cost modifiers using QFD model as discussed in section 4.
5. Estimation of mold base cost (Cb), secondary elements cost (Cs) and core/cavity
material cost (Cm).
6. Final die/mold cost estimation using equation 5.
7. Listing quoted, actual and estimated costs (Table 9). The quoted cost is based on the
tool designer’s experience. The actual cost is accounted from operator’s machine
logbook records and manpower schedule. The estimated cost is determined from the
cost model.
8. Calculation of deviations for comparative evaluation.
Percentage of
deviation
Type of die /
mold
Case Product / die
Description
Quoted
price
‘Q’
Actual cost
(accounted)
‘A’
Cost
model
estimate
‘E’
(1-Q/A)
*100
(1-E/A)
*100
1 4-cavity I.M for
terminal block 1
50,000 48,300 46,520 -3.51 3.68
2 4- cavity I.M for
terminal block2
50,000 46,234 46,400 -8.14 -0.35
3 4- cavity I.M for
terminal block3
50,000 53,000 52,700 5.66 0.56
4 2- cavity I.M for
terminal block4
50,000 52,800 48,830 5.30 7.51
5 44 –cavity I.M for
cable ties (150I)
2,00,000 1,93,500 1,82,000 -3.35 5.94
6 36 – cavity I.M for
cable ties (200I)
2,00,000 1,86,000 1,84,650 -7.52 0.72
Injection
molds
7 Single cavity I.M for
pump impeller
2,50,000 2,40,300 2,38,000 -4.03 0.95
8 Single cavity PDC die
for fan cover type-I
1,30,000 1,25,000 1,25,500 4.00 -0.4
9 Single cavity PDC die
for fan cover type 2
1,35,000 1,28,450 1,32,400 -5.09 -3.07
Pressure die
casting dies
10 Single cavity PDC die
for top cover
1,70,000 1,74,000 1,75,637 2.29 -0.94
11 2- cavity wax mold for
rear sight
35,000 33,650 36,200 -4.01 -7.57Wax injection
molds
12 Single cavity wax
mold for bracket
45,000 46,100 44,890 2.38 2.62
Rubber
compression
mold
13 Split mold for face
piece of rubber
oxygen mask
2,30,000 1,98,000 2,06,600 -16.16 -4.34
Mean deviation -2.47 -0.40
Table 9: Results for different case studies (costs in India Rupees)

18. 18
The cost deviations of the two methods: intuitive method (used for quotation purpose)
and the proposed cost model, were calculated and compared (Figure 5). The average
deviation of estimated cost from actual cost is found to be 0.4% for the proposed cost
model compared to 2.5% for the intuitive method. The maximum deviations are 2.5%
for the proposed model compared to 16% for the intuitive method. An additional
exercise was to study the effect of overall complexity of the molds on cost deviation.
For this purpose, the examples were sorted in 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
It is seen from figure 5 that the proposed model gives better results than intuitive
method for complex molds, in which accurate cost estimations are more important
owing to the higher costs involved. The proposed cost model also appears to be more
flexible, and can be easily customized to individual tool room practices by establishing
their own ratings for cost modifiers.
-20
-15
-10
-5
0
5
10
1 2 3 4 5 6 7 8 9 10 11 12 13
Examples
Percentagedeviation
(1-Q/A)100
(1-E/A)100
Figure 5: Cost deviation comparison
7. CONCLUSION
Die and mold development procedure varies from part to part and is not very well
documented. The conventional cost estimation methods depend on the experience of
the toolmaker and may not yield realistic estimates, especially when die complexity is
high. In this work, feature based approach, activity based costing and parametric
costing methods were integrated to develop a hybrid die/mold cost estimation model.
This cost model is flexible and project specific, yet easy to apply. A quality function
deployment approach has been proposed for customizing the tooling cost modifiers.
This enables incorporating the experience of the cost appraiser as well project-specific
complexity indicators. The proposed cost model has been validated on 13 industrial

19. 19
examples, including injection molds and pressure diecasting dies. The average
deviation was only 0.40% and the maximum deviation was 7.6%.
The proposed cost model forces a systematic approach, which may be difficult to
implement in smaller tool rooms. Secondly, feature identification and complexity rating
for customizing the cost modifiers require some expertise and experience. Integrating a
computerized database of previous cases, along with automated feature recognition can
overcome the above limitations and also enhance the efficiency of the proposed cost
model. This is presently being investigated.
8. ACKNOWLEDGEMENTS
The authors would like to acknowledge the Tool And Gauge Manufacturers
Association (TAGMA), Mumbai, India for sharing the information on status of Indian
die and mold manufacturing industries. The cooperation of the staff of Manufacturing
Technology Group, Central Mechanical Engineering Research Institute Durgapur in
die/mold development and establishing the machining process constant is also
acknowledged.
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20. 20
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