23590459 Assembly for Die Making

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Robotics and Computer-Integrated Manufacturing 22 (2006) 409–419

Computer-aided assembly planning for the diemaking industry

Antonio Armillotta, Giovanni Moroni, Marco RasellaÃ

Dipartimento di Meccanica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy

Received 7 October 2005; accepted 28 November 2005

Abstract

The paper describes a CAD-based approach to some management tasks related to the manufacture of stamping tools for car body

parts. The proposed method generates assembly plans for draw dies and trim/pierce dies from their design information. Die assembly is ahighly constrained process including a variety of part handling, measurement and surface finishing operations. As such, it escapes some

critical assumptions underlying most generative planning methodologies in literature. Such complexity is faced through a comprehensive

description model of the assembly process, which represents the space of all feasible operation sequences for any allowable die

configuration within a predefined domain. Once a specific configuration has been retrieved from solid and surface CAD descriptions of

die parts, the assembly model is instantiated into a graph-like data structure, which include only applicable operations and their

precedence constraints. An instance describes operations at a sufficient detail level to support time estimation, process documentation

and production scheduling. A prototype software tool derived from the assembly planning method has been tested in a real industrial

context, in order to evaluate its potential impact on the efficiency of die manufacture.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Die manufacturing; Assembly; Process planning.

1. Introduction

Tooling is the main cost factor in the production of

conventional steel parts of car body. One of the reasons for

the high cost of forming dies used for outer panels and

underbodies is the large amount of work needed to

assemble them. A typical die (Fig. 1) is made by dozens

of mechanical parts mounted on four main large-sized, cast

elements (lower shoe, blankholder, punch, upper die).

Within die manufacturing, the assembly process is a

complex set of activities, which follows CNC machiningof cast elements. It includes a number of very different

operations, from part handling and fastening to bench

machining, thermal treatment, dimensional control, sur-

face finishing and die tryout. The whole process takes up a

great deal of personnel and machinery resources for several

months. Its efficient running is critical to avoid cost

overruns and delays on production schedules.

During last years, diemaking companies have been

improving their engineering, procurement and in-house

machining practices through an increasing CAD/CAE/

CAM integration. To date, few efforts have been done to

extend the benefits of modern product data management to

the assembly stage. According to the above considerations

on process complexity, significant margins for time and

cost reduction could be gained through the adoption of a

structured methodology referred to as computer-aided

assembly planning (CAAP). As proved in different

industrial contexts, the implementation of an integratedCAD/CAAP software tool could be helpful to optimize the

assembly process for throughput and resource utilization.

The present paper describes a method for the assembly

planning of draw dies and trim/pierce dies for sheet metal

body parts. The development of a CAAP tool is proposed

as an interfacing link between the design of a die and the

operational management of its assembly process. The

purpose of such integration is to streamline or automate

several tasks, which are usually carried out by an unformal-

ized, empirical approach. These include preparation of

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ÃCorresponding author. Tel.: +39 02 2399 4925; fax: +3902 70638377.

E-mail address: [email protected] (M. Rasella).

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blueprints and other process documents, allocation of

resources to manufacturing projects and scheduling of

assembly work (Fig. 2).

The proposed method has been developed within a

specific industrial context, referring to an Italian company

with a leading position in the field of automotive

diemaking (FontanaPietro SpA). The contribution of the

company has been essential for a clear understanding of

key issues related to process modeling. It has also helped to

define software specifications and to test results on real

cases. However, it is claimed that the followed approach

can be proposed for similar problems in a wider class of

applications.

The remainder of the paper is organized as follows.Sections 2 and 3 provide an overview of the method and

justify the approach with respect to related literature.

Sections 4–6 describe the three basic elements of the

planning architecture, namely the extendable knowledge

base, the extraction of CAD data and the generation of

assembly plans. Sections 7 and 8 discuss the results of

validation tests on the method and identify possible

extensions of the work.

2. Related work

The assembly-planning problem has been widely studied

since the introduction of commercial CAD packages based

on solid modelers [1]. Its relevancy to the manufacturing

industry was first recognized when robots with suitable

accuracy to the execution of fine-positioning tasks became

available. While paving the way for flexible automated

assembly, this innovation suggested the idea that a robot

line could be automatically reconfigured according to the

geometry of parts to be assembled. Early CAAP methods

were dedicated to task-level robot programming, i.e.

generation of a high-level sequence of operations for each

robot cell in an assembly system [2,3]. In a later phase, the

interest moved to different applications including product

design for ease of assembly [4,5], selection and design of assembly systems, management of assembly lines [6,7].

A fast, data-driven definition of feasible operation

sequences is a critical condition for the efficient fulfilment

of all these tasks.

Most methods proposed in literature focus on auto-

mated assembly processes. These lend themselves to a

relatively simple geometric description, since robots lack

sufficient dexterity to complex motion trajectories and

ability to react to complex sensory information. Few

studies have investigated on reasoning criteria to describe

manual assembly, which poses special difficulties such as

more complicated part handling, two-handed manipulation

and the need to consider ergonomic requirements [8,9].

The search for assembly plans is usually carried out by a

generative approach, which involves direct reasoning on

part geometry with little or no reliance on domain-

dependent information. Most available methods share a

common architecture deriving from general-purpose plan-

ning methods proposed in the field of artificial intelligence

[10]. In a first step, product data are analyzed to extract all

information concerned with the assembly process. This

always includes the definition of contact relations among

parts, typically expressed through a graph-like structure.

Based on geometric and relational information, combina-

torial algorithms generate a new structure (precedence

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Fig. 2. CAD integration in die assembly.

Fig. 1. Single-action draw die for outer body panels (courtesy: Fontana-

Pietro SpA).

A. Armillotta et al. / Robotics and Computer-Integrated Manufacturing 22 (2006) 409–419410



graph, AND/OR graph, state transition diagram) repre-

senting an ordered set of feasible assembly sequences

[11–13]. A final optimization step may select one or more

optimal plans according to such criteria as cycle time

minimization and availability of assembly tools in the

system [14,15].

The main shortcoming of the generative approach is thecomputational complexity of the problem, whose solution

space grows exponentially with part count. To overcome

this difficulty, the problem has been faced under simplify-

ing assumptions recognized as consistent with foreseen

applications. Specifically, assembly plans are bound to be

sequential (only a part or subassembly at a time is

manipulated) and linear (each part reaches its final

assembly position by just one manipulation). Moreover,

sequence generation algorithms are seldom able to treat

assemblies with large numbers of parts. Another typical

limitation of generative planners is the difficulty to enhance

reasoning with technical knowledge related to the specific

product to be assembled. Little attention seems to have

been paid to the variant approach, which has been

successfully attempted to tackle highly domain-dependent

problems in the planning of machining processes.

Despite the availability of CAD/CAM tools dedicated to

diemaking, few studies have pursued the development of a

seamless integration between die design and manufactur-

ing. Computer-aided process planning methods have been

recently proposed to streamline the CNC machining of

casting patterns and free-form surfaces of main die

elements [16–18]. A method for the generation of assembly

plans for stamping dies has been proposed to enable

simulations and design evaluations on product assembl-

ability. It implements a generative search for feasible

operation sequences from adjacency relations among die

components, without any reference to non-assembly

operations involved in the typical assembly process of

automotive dies [19].

3. Approach

Some assumptions underlying generative planning meth-

ods are not satisfied in the assembly of large sized, highly

accurate tools such as stamping dies for the automotive

industry. Nonlinear plans result from the frequent need to

temporarily fasten parts for the sake of dimensional

measurements or functional tests, and then take them

apart to allow remachining or finishing operations.

Domain-dependent information is often critical in the

organization of the assembly process, due to the unique

functional requirements of forming tools compared to

generic mechanical products. Constraints to feasible

assembly sequences derive from both corporate practices

and specifications of car manufacturers.

As a consequence of these special requirements, the

assembly process of a die cannot be regarded as a mere

sequence of part-handling operations, and cannot be

analyzed through purely geometric considerations. Due

to the diversity of operations and resources involved, its

structure resembles a complex project made by a set of

activities subject to precedence constraints. A feasible

assembly sequence consists in each allocation of activities

to resources, which does not violate project constraints.

For any given die configuration, a CAAP tool must build a

formal description of the project, which represents the set

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Fig. 3. Overview of the CAAP system.

A. Armillotta et al. / Robotics and Computer-Integrated Manufacturing 22 (2006) 409–419 411



of feasible assembly sequences. Such description should be

sufficiently clear to convey process information, and

sufficiently complete to allow process optimization.

However, there is a logical difference between the output

of assembly planning and PERT/CPM charts or similar

work breakdown structures. While the latter are inherently

static models, assembly operations and their precedencerelations must be explicitly evaluated as a function of die

configuration. In the proposed approach, the assembly

description for a die is built as an instance of a

comprehensive process model through the use of CAD

data. Specific views of the process are generated from the

instance according to the needs of supported applications

(documentation, time and cost estimation, scheduling). The

architecture of the CAAP system, shown in Fig. 3, is

defined by the following concepts:

Model : is the set of all assembly process descriptions for

any allowable die configuration within a predefined,

company-specific domain of variants; it contains de-

tailed information regarding assembly operations, which

are arranged into a hierarchy of phases and subject to

precedence constraints; the information is stored in an

implicit form, i.e. it depends on die design parameters.

Instance: is the explicit description of the assembly

process for a die; it is built from the process model

through the extraction of assembly-related data from die

CAD models; the instantiation resolves model alter-

natives, eliminates inapplicable operations and prece-

dences, and evaluates the execution time of each

operation from parameter values defined in die config-

uration. Views: are subsets of information selected from a model

instance and transferred to all interested subjects (design

and manufacturing engineers, shop-floor foremen and

operators); they include an estimation of time and cost,

an assembly plan and an interactive graphical descrip-

tion of assembly operations.

4. Process modeling

Through a preliminary analysis of all kinds of activities

involved in the manufacture of draw dies and trim/pierce

dies at the reference company, a descriptive model of

the assembly process has been built. As shown in Fig. 4,

the model is organized as a network of relations among

lists of four basic entities: die parts, available resources,

assembly operations and occurrence conditions associated

to them.

The operation list includes any operation that couldbe involved in the assembly process of a die within the

given domain. Information related to each operation

includes:

type and amount of needed resources (operators,

machine tools, presses, coordinate measuring machines,

material handling and other kinds of equipment),

selected from a list of available resources;

precedence relations with other operations, given their

applicability in a feasible assembly sequence; these

constraints can also involve phases, i.e. sets of opera-

tions to be carried out consecutively by the same

resources on the same die element; an occurrence condition, selected from a list of

predefined ones; conditions may simply invoke the

presence of one or more die parts, or involve constraints

on the values of part attributes;

one or more time estimators, which express execution

time as a function of part attributes; in the simplest case,

typical of part-fastening operations, the function multi-

plies a unit time by the number of handled parts; in

other cases, such as surface-finishing operations, it can

involve a different mathematical function of one or

more geometric attributes (e.g. surface areas and edge

lengths of the main die elements); alternative estimationfunctions defined for the same operation can be selected

according to conditions on die parts.

Part attributes are needed to check occurrence condi-

tions and to calculate the execution time of operations.

Attributes have a different meaning for each part type, and

relate to such properties as size, shape complexity,

mechanical construction and location in die assembly.

For the main cast elements, attributes may be related to

specific geometric properties: for example, areas of free-

form surfaces on die elements (punch, die, blankholder)

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Fig. 4. Model of the assembly process.




influence the duration of most-finishing operations on the

same surfaces. Attributes defined for all parts and castelements define the set of information to be extracted from

the CAD description of a die.

A demonstration of how part attributes are used in

process planning is shown in Fig. 5. The part in the

example (a die button for a trim/pierce die) is identified by

four attributes. Three of them (mounting part, size, shape)

take discrete values referring to choices among options,

while the latter (area of freeform surface) can take any

value within a continuous range. The assembly process for

the die includes some operations, which involve the

existence of at least one part of this type. At a first stage,

attribute values are used to check the occurrence of these

operations in the actual assembly plan. Afterwards, they

are used to select one of the available functions for the

estimation of operation time, as well as to provide the

values of parameters used in the selected function (con-

stants appearing in time estimators have been altered for

confidentiality reasons).

The model of the assembly process forms the knowledge

base for the planning method. In the software prototype,

the information contained in the model can be changed and

updated to accommodate for new part types and technical

improvements in the manufacturing process, whose impact

on the whole process can be readily evaluated with the help

of the CAAP tool.

5. Assembly data extraction

The bulk of design data on a die contains a number of

assembly related attributes. They need to be extracted in

order to define the corresponding assembly plan and to

estimate the time needed for the execution of each single

operation within the plan. The main source of information

for this task is the CAD model of the die, which can be

regarded as a hierarchical data set arranged on four levels:

die assembly, main elements, components and surfaces.

Specific kinds of attributes can be extracted from each

different level.

Data extracted at assembly level include the type

and basic dimensions of the die. Identification of die

type (draw or trim/pierce, single or double-action, for

outer panels or underbodies) allows a selection of the

applicable process model. Dimensional data, evaluated

with reference to a bounding box of the assembly,

are needed to estimate standard times for such types

of operations as CNC programming and dimensional

control.

Information collected at the second level is aimed at

checking the presence of all main die elements. These

include the cast parts already shown in Fig. 1 for a draw die

and the corresponding parts (lower and upper shoe,

blankholder, blanking and piercing inserts) for trim/pierce

dies. Missing die elements are usually related to incomplete

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Fig. 5. Part attributes and their use in process modeling.




die configurations, which are likely to result into useless

assembly plans.

The third level consists in the solid models of the single

components to be mounted on main die elements. Guide

pins, standoffs, gas cylinders, and lifters are examples of

these kinds of components. As a first step, for a correct

reconstruction of the assembly plan from the processmodel, the presence of each type of component on the die

has to be detected. To avoid the resort to complex

recognition rules, components are identified from basic

model properties, expressed as either numeric of string-

based data. Such information is arranged into a proper

format and provided by product engineers at design stage.

Further information, needed to correctly estimate the

execution time of each single assembly operation, include

part type, shape complexity and size. To extract type and

complexity data, appropriate properties have been defined

and included in part models. These allow to recognize

either commercial or company-specific variants for many

components. For example, jacks used to lift the sheet metal

part after the drawing cycle are available in three different

types (T-shaped, cylindrical, air spring), which are distin-

guished from a ‘type’ attribute in model properties. Size

attributes, which affect execution time for many part-

mounting operations, are recognized from model bounding

boxes, extracted through dedicated calculation procedures.

Fig. 6 shows the extracted data for a sample part (the

blankholder of a draw die).

The fourth level, which includes face models, provides

several kinds of geometric data on die components. These

include the areas of surface regions and the lengths of edges

involved in some operations (free-form surface, sharp

bending corners, contours to be deburred after CNC

machining). Such data are essential to estimate execution

times for most machining and finishing operations. For

example, the time needed to finish a punch surface is

estimated as proportional to its area; while the time needed

to polish the blankholder surface is estimated as a linear

model of punch edge length and of upper die surface area.This linear model is obtained from a regression study.

A software module has been developed within the

CATIA V5 user interface to directly extract these

information from CAD data and to transfer them to the

planning module. A recursion-based search allows to

explore the whole logical tree of die CAD model regardless

of its design history. The resulting data structure, stored in

an appropriate file format, contains all information needed

to plan a correct assembly sequence and to calculate

operation times.

6. Instantiation and view generation

In the developed prototype, the final steps of assembly

planning are carried out by a stand-alone software module.

This choice creates a logical separation between the

treatment of product geometric data and manufacturing

information. While the former is naturally linked to the

software design platform, the latter is most often accessed

out of the CAD environment. In the perspective industrial

deployment of the software tool, this choice allows also

updates of the planning module to occur independently of

future releases of the CAD package.

As said before, information transferred to the planning

module lists die parts and related attributes. Each parts is

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Fig. 6. Assembly-related data for a die component.




recognized by its identifier as belonging to a given part

type. Values of part attributes are assigned to parameters

needed for planning calculations through rules depending

on part type. For verification purposes, the user is provided

with a list of parts to be mounted on main die elements(Fig. 7).

As some planning parameters do not refer to informa-

tion contained in CAD models, user data are provided in

addition to extracted part attributes (Fig. 7). These include

specifications on die materials, type of sheet metal part

(outer panel or underbody), thermal treatments and

stamping tests, as well as some operation times, which

have been evaluated off-line by manufacturing engineers.

Starting from both extracted and user-provided data, the

planning module builds the instance of the assembly

process for the die. Applicable operations and related

resources are selected through an evaluation of occurrence

conditions based on die data. Similarly, subconditions for

estimator selection are also verified in order to calculate the

execution time for each applicable operation. Precedence

relations among applicable operations are calculated by

pruning the constraint set of the overall assembly model.

The instance is then stored in a convenient format for the

generation of user views.

A list of estimated operation times is output as a first

view deriving from the instance. It provides an overview of

how total assembly time is spread among different types of

activities. It also allows to evaluate the workload for each

available resource for the completion of the die. In the

reference company, this information is critical to allocate

jobs to manufacturing units. Based on unit costs of

resources, a cost breakdown is also generated to identify

margins for process improvements.

A second instance view provided by the software tool is a

graphical chart of the assembly plan, which showsprecedence relations among operations and allows to

visually spot which phases of the assembly process can

occur simultaneously on different die elements (Fig. 8). The

chart identifies operations of the critical path, which

deserve special attention in order to avoid delays on

assembly lead time. Such information is helpful to gain a

complete understanding of the assembly process for both

managers and engineers. For this purpose, users can also

request details on operation times and resources. Informa-

tion presented through the user interface can be output in

the form of worksheets useful for the preparation of paper

documentation on the assembly plan.

A third view provided by the software is a graphical

description of assembly operations. This is obtained

through a sort of process ‘movie’ which illustrates, for

each assembly phase and for each main die element, the

path followed by single components from a die external

position to final mounting position (Fig. 9). The digital

animation is automatically created from a set of images

that show the components at discrete position steps. This

information is helpful for operators to gain a complete

understanding of each single assembly operation. For this

purpose, operators are allowed to interact with the

animation by changing the point of view and the zoom

level.

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Fig. 7. Summary and editing of basic die information.




7. Results

Tests carried out on a first prototype of the CAAP

software tool have allowed a first verification of process

models and procedures for instantiation and view genera-

tion. It has been noted that preliminary data collection and

comparison of results to industrial practice are essential

phases for a critical analysis of assembly activities. As a

result, process knowledge has been formalized at a more

detailed level than usually experimented in die manufac-

ture. As in most projects aimed at implementing computer-

aided planning, such effect is at least as important as are

practical benefits achieved in production management

activities.

In order to validate the effectiveness of the proposed

approach, some test cases provided by the partner

company have been processed by the assembly planner.

They account for the main variability factors that could be

found in draw dies and trim/pierce dies with respect to die

size and configuration as well as combinations of part

count, types and sizes. For each case, the software tool

has provided an estimation of lead time and total work

content of the assembly process. For some sample dies,

Table 1 shows the differences between estimated values

of these entities to actual figures recorded at manufactur-

ing stage.

Estimates have been recognized as sufficiently accurate

for all intended applications. Specifically, resource alloca-

tion to manufacturing divisions is usually done under

comparable tolerances on cycle time. The achieved benefit

is especially in the short response time of the CAAP tool.

Time estimates are immediately generated without the need

of any additional time delay at design stage. For the same

task, former practice required a few workdays to be spent

on each different die for paperwork and data collection

through interactive queries on CAD models. This need

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Fig. 8. Schematic illustration of the assembly plan.




used to result in significant personnel costs due to the high

number of dies engineered at the company.

Moreover, corporate managers have been provided with

documentation that was not previously available. This

includes assembly plans in worksheet format and calcula-

tion of workloads on the different functions of the

assembly division (part handling, measurement, tryout

etc.). In the long run, such information will help to gain

insights for an efficient management of manufacturing

personnel.

Software-generated information about precedence rela-

tions has allowed to build some Gantt charts of assembly

operations. They could be essential tools for a thorough

revision of assembly processes, whose bottlenecks can be

clearly identified. In the example of Fig. 10, the lead time of

the assembly process is clearly affected by three-time

consuming operations in the die tryout phase. The evidence

of their impact on process efficiency suggests the need to

consider technological alternatives that could ensure the

proper accuracy and surface finish on the die in accordance

with customers’ specifications. In general, such a time

planning cannot be directly used for the scheduling of

manufacturing activities, which always deals with common

resources shared by multiple projects. However, it can

suggest corrective actions, which can remove constraints

and streamline the whole flow of activities.

8. Conclusions

The use of the software prototype has confirmed the

expectations, which suggested the development of anassembly planning method. The most immediate impact

of the work is likely to affect manufacturing activities.

Managers involved in resource allocation decisions

can be provided with more responsive and accurate

estimations of assembly time and cost. For manufacturing

engineers, a CAAP tool is a valuable support to decisions,

which are often taken without full information about

process variants imposed by specific dies. Assembly

operators can find interactive graphical information more

effective than paper blueprints for a quick understanding

of daily tasks.

As a positive side effect for diemaking companies,

implementation of assembly planning is a means to

collecting explicit corporate knowledge, which may be

scattered among employees and paper archives. Moreover,

due to CAD integration, many manufacturing evaluations

can be anticipated at design stage. This enhances the ability

of design engineers to identify critical issues and process

bottlenecks, thus acquiring additional design criteria for

new dies. This opportunity will be better tested during the

first new projects that will be carried out with the help of

the CAAP tool.

Future developments of the method will involve an

extension of assembly planning to different die types and a

complete testing of the software tool after its final

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Fig. 9. Video animation of mounting phases.

Table 1

Test results on the CAAP software prototype

Case Difference on lead

time [%]

Difference on total

work hours [%]

Die 1 À8.8 2.9

Die 2 À5.8 À7.7

Die 3 À7.5 0.4




deployment at the partner company. Some open issues,

related to estimation and optimization of specific assembly

operations, will be addressed with the aim of developingimproved CAD-based solutions. Efforts will also be

dedicated to interfacing assembly planning to a compu-

ter-aided tool for the scheduling of the assembly

process. This will support an optimized management of

manufacturing units through the use of assembly process

information related to all dies to be assembled at the

company in a given planning horizon.

Acknowledgements

Financial support to this work has been provided by

MAP (Italian Ministry of Production Activities), MIUR(Italian Ministry of University and Research) and Fonta-

naPietro SpA, Calolziocorte (LC). The authors wish to

gratefully acknowledge the helpful collaboration of Dott.

F. Tagliabue and of the whole R&D FontanaPietro SpA

staff.

References

[1] Abdullah TA, Popplewell K, Page CJ. A review of the support tools

for the process of assembly method selection and assembly planning.

Int J Prod Res 2003;41(11):2391–410.

[2] Weule H, Friedmann T. Computer-aided product analysis in

assembly planning. CIRP Ann 1989;38(1):1–4.

[3] Zussman E, Lenz E, Shpitalni M. An approach to the automatic

assembly planning problem. CIRP Ann 1990;39(1):33–6.

[4] Hsu W, Fuh JYH, Zhang Y. Synthesis of design concepts from adesign for assembly perspective. Comput Integr Manuf 1998;11(1/

2):1–13.

[5] De Fazio TL, Rhee SJ, Whitney DE. Design-specific approach to

design for assembly (DFA) for complex mechanical assemblies. IEEE

Trans Robotic Autom 1999;15(5):869–81.

[6] ElMaraghy WH. Integrated assembly planning and scheduling—

CAPP-related issues. CIRP Ann 1992;41(1):11–4.

[7] Grewal S, Tran P, Bakshare A. Assembly planning software. CIRP

Ann 1995;44(1):1–6.

[8] Bullinger HJ, Rally PJ, Schipfer J. Some aspects of ergonomics in

assembly planning. Int J Ind Ergonom 1997;20:389–97.

[9] Wilson RH. Geometric reasonong about assembly tools. Artif Intell

1998;98:237–79.

[10] Fikes RE, Nilsson NJ. STRIPS: a new approach to the applica-

tion of theorem proving to problem solving. Artif Intell 1971;2:189–208.

[11] De Fazio TL, Whitney DE. Simplified generation of all mechanical

assembly sequences. IEEE Trans Robotic Autom 1987;3(6):640–58.

[12] Sanderson AC, Homem de Mello LS. Automatic generation of

mechanical assembly sequences. In: Wozny MJ, et al., editors.

Geometric Modeling for Product Engineering. New York: Elsevier;

1990. p. 461–82.

[13] Jones RE, Wilson RH, Calton TL. On constraints in assembly

planning. IEEE Trans Robot Autom 1998;14(6):849–63.

[14] Chen RS, Lu KY, Yu SC. A hybrid genetic algorithm approach on

multi-objective of assembly planning problem. Eng Appl Artif Intell

2002;15:447–57.

[15] Santochi M, Dini G. Computer-aided planning of assembly opera-

tions: the selection of assembly sequences. Robot Comp-Int Manuf

1992;9(6):439–46.

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most critical operations

Fig. 10. Gantt chart prepared from CAAP output.




[16] Shin H, Olling GJ, Chung YC, Kim BH, Cho SK. An integrated

CAPP/CAM system for stamping die pattern machining. Comput

Aided Des 2003;35(2):203–13.

[17] Choi BK, Ko K. C-space based CAPP algorithm for free-

form die-cavity machining. Comput Aided Des 2003;35(2):

179–89.

[18] Sheen D, Lee CH, Noh SD, Lee K. Process planning and NC-code

generation in manufacturing of press dies for production of car

bodies. Int J Prod Res 2004;42(1):37–49.

[19] Yang X, Ou Z, Lu P, Han F. Assembly sequence planning and

assembling simulation of stamping die tools. Proc SPIE 2003;4756:

274–9.

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23590459 Assembly for Die Making

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