Economical green product design based on simplified computer-aided product structure variation

Computers in Industry 60 (2009) 485–500

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Economical green product design based on simplified computer-aided product structure variation Chih-Hsing Chu a,*, Yuan-Ping Luh b, Tze-Chang Li a, Hom Chen a a b

Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, Hsinchu, Taiwan Institute of Manufacturing Technology, National Taipei University of Technology, Taipei, Taiwan

A R T I C L E I N F O

A B S T R A C T

Article history: Received 18 July 2007 Received in revised form 9 October 2008 Accepted 5 February 2009 Available online 17 March 2009

Environmental issues have become an imperative concern for most companies in relation to modern product development. Special procedures have to be taken during the product development process to comply with recent green directives. Product structure is recognized as a critical factor that provides effective means for reducing environmental impact in product end-of-life. However, most previous studies failed to leverage the vast latitude at the design stage due to the assumption of a fixed product structure. To overcome this deficiency, we propose a CAD-based approach that allows automatic variation of 3D product structure by means of changing the combination of parts, selecting the assembly method, and rearranging the assembly sequence. A computing scheme uses Genetic Algorithm (GA) techniques to produce an optimal product structure from the design alternatives generated by the approach. This corresponds to lower assembly/disassembly costs, while complying with specified recycling and recovering rates. The scheme also chooses a smaller set of parts that needs to be disassembled and determines an economical disassembly process. Implemented in a commercial CAD system, the test results demonstrated the effectiveness of this scheme in green product design in a costeffective manner. ß 2009 Elsevier B.V. All rights reserved.

Keywords: Green product design Product structure CAD Genetic Algorithms Metaheuristics

1. Introduction Environmental issues have become one of the critical challenges being faced by most developed nations and global brands. Several environmental directives, such as the Restriction of Hazardous Substances in Electrical and Electronic Equipment (RoHS) and the Waste from Electrical and Electronic Equipment (WEEE) have been effective since 2006. The electrical and electronic products sold to the European Union (EU) must comply with these. This produces a profound impact on modern product development and the management of the product value chain. Companies have begun to take proactive approaches at various stages of a product lifecycle in order to meet these green directives.  Product design: any attempts to make a product more environment-benign should be performed as early as possible in the product development process. Important factors that have to be considered at the design stage include the material selection

* Corresponding author at: Department of Industrial Engineering and Engineering Management, National Tsing-Hua University, 101 Kuang Fu Rd, Sec. 2, Hsinchu, Taiwan. E-mail address: [email protected] (C.-H. Chu). 0166-3615/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.compind.2009.02.003

for individual components, assembly method, and assembly sequence when constructing the product.  Manufacturing: the main effort is to eliminate toxic substances in the manufacturing process (e.g. development of lead-free operations). Due to a high degree of outsourcing, it is a big challenge to assure ‘‘green manufacturing’’ in the global supply chain. There is a lack of proper software tools for the declaration of green manufacturing accomplished in such a distributed manner.  Reuse and recycle: one key issue is choosing a disassembly sequence that fulfills the regulated recycling and recovery rates in an economical manner. Unfortunately, the improper selection of component material and assembly methods, which were determined at an early design stage, may seriously restrict the choices. Most complex consumer products need to be disassembled prior to reuse and recycling, which refer to the secondary use of components and materials, respectively. Many previous studies have investigated end-of-life product disassembly and the costs associated with the process. Das et al. [1] showed that the disassembly cost is mainly determined by a number of factors, like the disassembly operation time, the disassembly method, and more importantly, the disassembly sequence. A computational

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approach was developed for estimating the cost. A Disassembly Precedence Matrix (DPM) was proposed to characterize the spatial constraints among individual components of an assembly and to generate the possible disassembly sequences under those constraints [2]. One major limitation in DPM is that a component is only allowed to move in a fixed direction in disassembly planning. Various techniques have been used to plan the disassembly process of a 3D product. Gungor and Gupta [3] combined the DPM concept with a Branch and Bound algorithm. Kuo et al. [4] employed a part connection network and modular disassembly analysis to find the optimal disassembly sequence, and demonstrated the effectiveness of this approach in the end-of-life treatment of electronic products [5]. Assuming a sequencedependent disassembly cost, Lambert [6] proposed a modified two-commodity network flow approach that helps obtain a global optimum in the disassembly sequence planning. Gonzalez and Adenso-Diaz [7] developed a decision making mechanism based on the information contained in a bill-of-materials (BOM) for the product end-of-life decisions, with a focus on the waste treatment cost. Feldmann et al. [8] estimated the recycling rate based on the analysis results of a proprietary software tool and then suggested part material changes and/or modification of the assembly method, resulting in lower recycling costs [9]. Tseng and Chen [10] used personal computer upgrading as an example to illustrate how to implement a green design concept by proper part substitution. It is necessary to review computer-aided techniques concerning assembly and disassembly planning, as green product design is closely related to these processes. Smith [11] proposed multiple genetic operators that avoid premature convergence in GA-based assembly planning. The proposed method is, however, not integrated with CAD. The assembly constraints and interference detection among components must be given as a priori. Chao and Chen [12] analyzed assembly rules and constraints through the assembly relations of a product. These were then integrated with an assembly model and thus allow an evaluation of assemblability for the product configuration. Gottipolu and Ghosh [13] extracted geometric and mobility constraints from the CAD model of an assembly and translated them into two matrices. They also provided an algorithmic procedure that generates all feasible assembly sequences based on the matrices. Van Holland and Bronsvoort [14] developed an object-oriented modeling method for handling assembly features and connection interfaces. They demonstrated the effectiveness of the developed method on stability analysis, grip planning, and assembly sequence planning through the Delft Intelligent Assembly Cell (DIAC) project. Regardless of a fairly large amount of previous research on product end-of-life, most studies assumed a fixed product

structure in their methodologies for disassembly sequencing or the assessment of environmental impact. The analysis result was not fed back to the design stage for improving the early decisions in the product development. On the other hand, some literatures [8– 10] have demonstrated the significant influence of product design (e.g. changes in part materials and assembly methods) on fulfilling the green directives and realizing an environmentally conscious design. Nevertheless, how to accomplish the change still relies on human decisions and experience. There is a lack of systematic approaches to leveraging the latitude of the design activities. To address this need, this paper attempts to propose a computational approach that changes product structure for compliance with the green directives. Three variation mechanisms are provided to achieve this goal: changes in the combination of parts, assembly sequence, and assembly method. Any product structure variant generated as such is guaranteed to satisfy the product’s functional requirements and the assembly constraints for components. An optimization scheme based on Genetic Algorithms (GAs) is then applied to produce better product designs in terms of minimal environmental impacts. It also determines a smaller set of components that need to be disassembled and a cost-effective disassembly process. Test examples implemented in a CAD system demonstrate how the proposed approach improves product design by complying with the green directives in an economical manner. 2. Mechanisms for product structure variation Product structure (or architecture) is defined as a structural representation generated by mapping the product functions and physical components [15]. A three-step approach [16] has been proposed to determine product structure, consisting of (1) arranging functional elements, (2) mapping functional elements to physical components, and (3) defining interface specifications among components. We introduce a computational method based on a similar idea, which changes the product’s architecture using different mechanisms at each step. The following assumptions must hold in our method. First, a component database is given and consists of a set of candidate components that can be used to construct the product. The functional elements that each component can provide are given. 3D CAD models of those components already exist and the attributes related to the product assembly have been defined. As shown in Fig. 1, the proposed method varies a product’s structure based on three mechanisms: combinations of different components, changes in the assembly method, and rearrangement of the assembly sequence. Any product structure variant must satisfy the functional requirements of the product and the assembly constraints among its comprising components during the variation process. Each mechanism is discussed as follows.

Fig. 1. Approach for product structure variation.

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Fig. 2. Different product combinations.

2.1. Different component combinations A product can be composed of different component combinations as long as the components in each combination can provide all the product functions. Fig. 2 shows two variants of a ballpoint pen that consist of different sets of components. The number of components is also different in this example. Pre-defined part/ function mappings determine the feasibility of a chosen component combination. For example in Table 1, there are a total of p product functions (or functional elements) to be satisfied by aggregating those that the subset of n components and m subassemblies can offer. The function aggregation of any feasible combination must cover all the product functions f1, f2, . . . fp. Such a mapping can be characterized by systematic design methods, like the function-form method [16], QFD [17], or Axiomatic Design Principle [18]. 2.2. Definition of assembly features and interfaces There are different part assembly methods, such as welding, interference fit, adhesive bonding, and mechanical fastening. Each method requires a certain geometry or group of geometric elements to perform the assembly action. An assembly operation is usually specified with explicit rules like co-axis, co-plane, extended angle, and offset distance between geometric elements associated with 3D CAD models [19]. For simplification, this work only considers the assembly operations defined by the co-axis and co-plane conditions:  Co-axis: parts are assembled by aligning two linear axes.  Co-plane: parts are assembled by mating two planes with opposite normal vectors or by specifying a fixed distance between them.

Fig. 3 shows a solid model with three different feature volumes constructed in this manner: protrusion (E), depression (D), and through-hole (T). The through-hole feature is not limited to a round hole. The protrusion and depression features can provide both the co-axis and co-plane mating conditions for assembly, while a through-hole only contains a linear axis to be utilized. An assembly operation is accomplished by (1) aligning two axes and (2) specifying the relative position between two planes from different components. The final step is to fix the assembly by using the remaining degrees of freedom [20]. The planar face(s) to be mated is referred to as the assembly interface in this paper. In Fig. 4, the top face of P1 is an assembly interface that provides 10 assembly feature volumes of four different types: one protrusion (E), four large depressions (D1, D2, D3, D4), four small depressions (d1, d2, d3, d4), and one through-hole (T). P1 can be assembled with P2 and P3 based on different assembly conditions in the same feature interface. The P1/P2 assembly is completed by mating the four large depressions of P1 with the four protrusions of P2. In contrast, the four small depressions and the through-hole are used in the assembly with P3. Table 2 illustrates the effective feature volumes in both cases. To reduce the number of possible ways of constructing an assembly, the status of each assembly feature must be clearly defined in advance, i.e. the design engineer needs to specify whether or not each feature is allowed to be utilized during the variation process. The variation process consists of the following steps.  Step 1. Compatibility test between assembly interfaces: This step is to ensure that any two parts that are chosen possess a pair of compatible assembly features by checking whether any of the assembly features contained in one interface is able to match with that of the other interface. Step 2 will define the ‘‘match’’ conditions between different assembly features. Since a part may contain multiple assembly interfaces, we

We assume that a feature volume, positive or negative, is created by sweeping a planar profile along a given direction. This is a common way of feature creation in most modern CAD systems. Table 1 Part-function mappings.

p1 .. . pn A .. l . Am

f1

...

fp

1

...

0

0 0

... ...

1 0

1

...

1

Fig. 3. Cylindrical assembly features.

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Fig. 4. Different assembly conditions in Part P1.

assume that the one providing more feature volumes will be given higher priority, i.e. it will be first selected in the variation process and the selection proceeds to the parts with fewer interfaces.  Step 2. Change in assembly feature type: An assembly feature may change its original type after assembly with another feature. Possible type changes are summarized as follows:  Assembly between a protrusion (E) and a depression (D): two parts can be put together with a protrusion in one part and a depression in another as long as their cross section (profile) and length (depth) are identical. Both features will be set inactive after the assembly, which means they cannot be used in the subsequent assembly.  Assembly between a through-hole (T) and a compatible feature: we can distinguish three different conditions in this case.  A protrusion (E) and a through-hole (T): two parts can be assembled with a protrusion in one part and a through-hole in another as long as their cross sections (profiles) are identical. If the protrusion length is greater than the throughhole depth, a new protrusion feature will be created after the assembly. If the through-hole depth is greater than the protrusion length, a new depression feature will be created. Both features will be set inactive if their sizes match exactly. Fig. 5 illustrates these different conditions.  Two through-holes: two parts can be assembled with through-holes if their cross sections (profiles) are identical

(see Fig. 6(a)). A new through-hole feature will be created after the assembly. The depth of the new feature is equal to the sum of the individual depths.  A depression (D) and a through-hole (T): two parts can be assembled with a depression in one part and a through-hole in the other if their cross sections (profiles) are identical (see Fig. 6(b)). A new depression feature will be created after the assembly. The depth of the new feature is equal to the sum of the individual depths. Fig. 7 illustrates two different assembly results with the same two parts. If no valid assembly operation can be produced, another new pair of assembly interfaces will be selected in Step 1, and thus the variation process repeats until one valid assembly is generated

Table 2 Use of different assembly interfaces. P1a D1b Assembly operation With P2 H With P3 

D2b

D3b

D4 b

Tb

d1b

d2b

d3b

d4b

H 

H 

H 

 H

 H

 H

 H

 H

Fig. 5. Feature changes in an assembly with a protrusion and through-hole.

P2a

Assembly with P1

E1b

e1b

e2b

e3b

e4b



H

H

H

H

E2b

e5b

e6b

e7b

e8b

H

H

H

H

H

P3a

Assembly with P1 a b

Part name. Assembly feature.

Fig. 6. Feature changes in an assembly with a through-hole and depression.

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Fig. 7. Different assembly results with the same combination of parts.

or all the interfaces have been used up. Notice that a number of assumptions have been made in the algorithm to lower the computation complexity of the variation process. Thus the result cannot cover all the possible ways of assembly between two parts. 3. Evaluation of performance indices 3.1. Assembly cost A product can be constructed with different components and assembly methods. Different assembly methods usually incur different operation costs. For simplification, we only consider welding, adhesive bonding, and interference fit for the assembly. The total assembly cost of a product is expressed as: XX A Aln Cln (1) l

n

3.2. Green cost

8 < da  Dl ; A ¼ Cln fa  F l ; : ga  Gl ; XX j

where l: the index of a sub-assembly in a product; n: the index of different assembly methods (1 for welding, 2 for adhesive bonding, and 3 for interference fit); Aln: a binary value indicating that l is A assembled using the method n (0 for yes, 1 for no); Cln : the assembly cost associated with Aln; Fl: the length of the welding perimeter (if it exists) in l; Dl: the area of the adhesion surface (if it exists) in l; Gl: the feature volume providing an interference fit (if it exists) for l; fa: the welding cost of unit length; da: the adhesion cost of unit surface area; ga: the assembly cost of unit feature volume providing interference fit. The length of the welding perimeter, the area of the adhesion surface, and the volume of the feature providing an interference fit are the key parameters that determine the assembly cost. In addition, Eq. (3) implies that only one method can be used for assembly at a time.

n¼1 n¼2 n¼3

Aln ¼ 1

(2)

(3)

n

It is necessary to estimate the processing cost of a product’s end-of-life, referred to as the green cost, to satisfy the recycling and recovery rates in a cost-effective manner. The cost is determined by combining the disassembly cost to break up a product (or some subset of it) and the profit to sell the recycled parts. Previous studies have pointed out that the disassembly sequence is a critical factor for a cost-effective product end-of-life. It determines which components need to be disassembled, in what order they should be taken apart, and how to perform the disassembly processes. This research employs the DPM [2] approach to characterize the spatial relationships among the components to be disassembled. The matrix represents the primary constraints in the computation of the optimal disassembly sequence. This approach holds under the following assumptions:  A part cannot be rotated while being disassembled. Table 3 Best disassembly directions for each part.

Fig. 8. A disassembly example [2].

Part no.

Best disassembly direction

P1 P2 P3 P4 P5 P6 P7

[0]x [P5, P6, P7]+x [P1, P6, P7]x [P6]+y [P7]y [P3]+y [P4]y [0]+y [0]y

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 A part can only be disassembled along a single direction.  A part cannot be deformed in the disassembly process.  The positions of the rest of the parts remain intact while a part is being disassembled.  Only one part is disassembled at a time.  A part can be disassembled as long as the conditions defined in the DPM are satisfied. Fig. 8 is a simple example that illustrates how to define the disassembly constraints and to construct a DPM. There are a total of seven components to be disassembled in this product. To compute a disassembly sequence, follow the steps below:  Step 1. Determine the best disassembly direction: This step is used to determine the best disassembly direction by counting the number of the constraints for each part along different directions. As shown in Fig. 8, P2 will be blocked by (P5, P6, P7), (P1, P6, P7), (P1, P3, P6, P7), and (P1, P4, P6, P7) along the directions of positive x, negative x, positive y, and negative y, respectively. Therefore, the removal of P2 from the product will encounter the fewest constraints along the direction of positive x or negative x. Applying this concept to all the parts gives their best disassembly directions as shown in Table 3. The element value 0 indicates that there is no part blocking in this direction.  Step 2. Construct DPM:

Table 4 Definitions of recycling and recovery rates. Total mass of a product Mass of reusable parts (without further processing) Mass of recycled parts (further processing required) Recycling rate Mass of scrap parts (without reusable value) Recovery rate

A B C C/(A  B) D (D + C)/(A  B)

and the disassembly methods, in terms of minimized green cost, among all the possible sequences obtained in the previous step. The optimization process for a product’s end-of-life is expressed as: F X I X I X X Min d f  CicQ  Gi f

CicQ

i

c 2 Ci

8 < db  Di ; ¼ fb  F i ; : sb  X i ;

c¼1 c¼2 c¼3

F X df ¼ 1 f

This step computes the value of each element in the DPM. Given a part, we need to first identify its precedent components, i.e. the part (or parts) that must be disassembled before it can be removed from the product without interference. If such a component exists for the part being removed, then the corresponding element value is denoted as 1 in the DPM. The value becomes 0 when a part can be removed in any of the six directions (x, y, and z) without interference from any other parts. A more complex situation arises when a part becomes a precedent component only in some direction, e.g. P2 can be disassembled in x direction after P1 has been removed, as shown in Fig. 9. In this case, the value is set as x (see the second column/the first row).  Step 3. Generate disassembly sequences: All of the possible disassembly sequences for a product can be generated based on its DPM using the Branch and Bound Algorithm [3]. The recycling and recovery rates to be satisfied become termination criterion in the algorithm, i.e. it is possible that only a fraction of the components, not all, are disassembled in the sequences (refer to Table 4 for the definition of the recycling and recovery rates). In addition, the recovery rate acts as a constraint only when further disassembly is unprofitable.  Step 4. Construct the disassembly cost function: This step is to find an optimal (at least locally) disassembly process, including the sequence, components to be taken apart,

Fig. 9. Disassembly precedence matrix (DPM) of the example.

(4)

i

Fig. 10. Optimization of product structure using Genetic Algorithms.

(5)

(6)

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Table 5 Chromosome design for product structure.

Table 6 Chromosome design for assembly methods.

Level

Assembly index Assembly method

1 2 ... n

Part P1

P2

...

P(n)

1 0 ... 0

1 1 ... 1

... ... ... ...

0 0 ... 0

Gi ¼ Q i p  Z p

(7)

with constraints: PRCYC  PRCYC MIN f

(8)

CO PRCO f  PRMIN

(9)

where f: the index of a disassembly sequence; i: the index of a component to be disassembled in a product; c: the index of the different assembly methods (1 for welding, 2 for adhesive bonding, and 3 for interference fit); Ci: the assembly interfaces of component

A(1) MjA(1)

... ...

A(n1) MjA(n1)

i that need to be disengaged; df: a binary value indicating that the product is disassembled via the disassembly sequence f (0 for yes, 1 for no); Gi: the return profit by recycling part i; CicQ : the cost to disengage the assembly interface c of component i; p: the material index; Qip: the total mass of material p in part i; Zp: the unit recycle price of material p; Fi: the length of the welding perimeter in component i; Di: the area of the adhesion surface in component i; Xi: the length of the feature volume involved with the interference fit in component i; db: the unit cost of disassembling welding; fb: the unit cost of disassembling adhesive bonding; sb: the unit cost of disassembling interference fit; PRCYC f : the recycling rate of the : the recovery rate of the disassembly disassembly sequence f; PRCO f : the minimum recycling rate regulated by the sequence f; PRCYC MIN green directives; PRCO MIN : the minimum recovery rate regulated by the green directives.

Fig. 11. Chromosome representation of a simple product.

Fig. 12. Crossover operator for assembly sequence.

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Fig. 13. Crossover operator for assembly method.

We assume that a destructive method, such as cutting, is needed to separate the welded and bonded interfaces. Hence, multiplying the length of the assembly interface by the unit cost incurred in the cutting operation gives the disassembly cost. An interference fit requires a special operation, like heating and expansion, in disassembly. The resultant cost is in proportion to the length of the feature volume providing the interference fit. The definitions of the recycling and recovery rates are shown in Table 4.

This section has described the prerequisite tasks for conducting the optimization process. The product development costs mainly consist of the assembly cost and the disassembly cost, defined in Eqs. (1)–(3) and Eqs. (4)–(7), respectively. The corresponding constraints and the assumptions behind them were also discussed. Different mathematical programming techniques can be applied to obtain the optimum solution with the representations introduced by this section. The next section

Fig. 14. Mutation operator for assembly sequence.

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Fig. 15. Mutation operator for assembly method.

explains how we employ a GA-based approach to generate a better product structure compliant with the given green directives. 4. Optimizing product structure 4.1. Genetic Algorithm Many combinatorial optimization problems from product design and manufacturing are too complex to be solved using conventional optimization techniques. A set of metaheuristic algorithms (simulated annealing, Tabu search, Genetic Algorithms) have thus been proposed to find approximate solutions for these problems. Among them, GA is a class of stochastic search algorithms based on the concept of biological evolution [21]. Due to the combinatorial nature of the product variation, we chose

GA for computing an optimal product structure for green design. Fig. 10 illustrates the optimization process. The objective is the total cost, including the assembly cost, the disassembly cost, and the recycling profit if it exists. The major constraint is complying with the given recycling and recovery rates. A better product structure is generated using the variation mechanisms described in Section 3, integrated with the Genetic Algorithms described below. 4.2. Chromosome representation for product structure One critical task in any Genetic Algorithm is the design of chromosomes. We are going to use two different chromosomes in the product structure variation. The first one represents the product structure by encoding the combination of different parts and their assembly methods simultaneously. As shown in Table 5,

Fig. 16. Implementation framework for green product design.

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Table 7 Candidate parts that comprise the first test example.

n is the total number of components comprising the product. Given a component Pi at some level, the first gene value 1 indicates that the component and the next component Pi+1 will be assembled at that level. The gene values corresponding to the other components at the same level will be ignored. No operation is conducted for a gene value of 0. Such chromosome design only allows the assembly of two parts at a time. The assembly of three (or more) parts can be decomposed into a sequence of two-part assembly operations. Table 6 shows the chromosome design that represents different assembly methods. The gene values MjA of 1, 2, and 3 correspond to the assembly methods of welding, adhesive bonding, and interference fit, respectively. Fig. 11 shows an example that illustrates the correlation of the two chromosomes to the product structure. Note that this representation scheme does not reflect the assembly validity represented by a chromosome, i.e. whether part interference occurs. This validity issue will be handled by the fitness function later on.

during the evolution process. The fitness function is expressed as: evalðck Þ ¼ C asm þ C green þ C penalty

(10)

where ck represents the kth chromosome. It is possible for invalid assembly to occur in the following situations: (1) no assembly interfaces are compatible, (2) part interference, and (3) violation of the part precedence relationship. Each one corresponds to a cost term in the penalty function, expressed as: C penalty ¼ PenaltyFuncðck Þ ¼

AðC Xk Þ

AssemErrorCost þ

a¼0

þ

CðC Xk Þ

BðC Xk Þ

IntersectCost

b¼0

SequenceErrorCost

(11)

c¼0

4.3. Fitness function definition The design of the fitness function mainly considers the assembly cost, the green cost, and the penalty cost. The previous sections have explained the first two parts. The penalty function is included to account for the invalidity of an assembly

where A(Ck), B(Ck), and C(Ck) represent the total number of assembly failures occurring in a product structure due to interface incompatibility, interference, and precedence violation, respectively. Each penalty cost is a fixed value given by the user.

C.-H. Chu et al. / Computers in Industry 60 (2009) 485–500 Table 8 Required design functions of a ballpoint pen. F1 F3 F5 F7

Housing Align ink cartridge Facilitate pen carrying Improve aesthetic appearance

495

Table 10 All the valid part combinations and the costs of the individual optimal structures. F2 F4 F6 F8

Fix ink cartridge in the pen Shield ink cartridge Store ink Improve comfort in use

Table 9 Assembly and disassembly costs. Assembly method

Assembly cost

Disassembly cost

Welding Adhesive bonding Interference fit

70 Dollar/cm 5 Dollar/cm2 20 Dollar/cm3

80 Dollar/cm 80 Dollar/cm 5 Dollar/cm

Part combination

Total cost

(Barrel #4, Tip Barrel #1, Cap #1, Cartridge) (Barrel #3, Tip Barrel #1, Head Barrel, Cap #1, Cartridge) (Barrel #1, Tip Barrel #1, Cap #1, Cartridge, Rubber Grip) (Barrel, Tip Barrel #1, Head Barrel, Cap #1, Cartridge, Rubber Grip) (Barrel #7, Tip Barrel, Cap #1, Cartridge) (Barrel #5, Tip Barrel, Head Barrel, Cap #1, Cartridge) (Barrel #11, Tip Barrel #1, Cap, Cartridge) (Barrel #9, Tip Barrel #1, Cap, Cartridge, Rubber Grip) (Barrel #6, Tip Barrel #1, Head Barrel, Cap, Cartridge) (Barrel #4, Tip Barrel #1, Cap, Clip, Cartridge) (Barrel #3, Tip Barrel #1, Head Barrel, Cap, Clip, Cartridge) (Barrel #2, Tip Barrel #1, Head Barrel, Cap, Cartridge, Rubber Grip) (Barrel #1, Tip Barrel #1, Cap, Clip, Cartridge, Rubber Grip) (Barrel, Tip Barrel #1, Head Barrel, Cap, Clip, Cartridge, Rubber Grip) (Barrel #10, Tip Barrel, Head Barrel, Cap, Cartridge) (Barrel #8, Tip Barrel, Cap, Cartridge) (Barrel #7, Tip Barrel, Cap, Clip, Cartridge) (Barrel #5, Tip Barrel, Head Barrel, Cap, Clip, Cartridge)

318.006 348.379 424.601 454.974 246.056 279.43 254.454 374.497 374.497 314.421 361.474 404.871 437.696 468.069 274.429 244.055 245.47 275.844

Fig. 17. Green-compliant optimal product structure for the test ballpoint pen.

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Table 11 Optimal product structure of (Barrel #8, Tip Barrel, Cap, Cartridge).

Table 12 Optimal product structure at lower recycling and recovery rates.

Total cost

244.055 Dollars

Total cost

245.47 Dollars

Assembly method

(Barrel #8, Tip Barrel): Interference Fit (Tip Barrel, Cap): Adhesive Bonding (Cartridge, Tip Barrel): Interference Fit Barrel #8, Cartridge 0.741291 0.741291

Assembly method

(Barrel #7, Tip Barrel): Interference Fit (Cap, Clip): Adhesive Bonding (Tip Barrel, Cap): Adhesive Bonding (Cartridge, Tip Barrel): Interference Fit Barrel #7, Cartridge 0.709668 0.709668

Disassembly sequence Recycling rate Recovery rate

Disassembly sequence Recycling rate Recovery rate

written as:

4.4. Design of evolution process Genetic operators (crossover and mutation) are applied to produce a second generation population in GA. A selection probability (Pk) is generated for each chromosome. A pair of parent chromosomes is selected to generate a new solution in the current generation by the roulette wheel selection mechanism. The equation for calculating the probability is

P k ¼ PN

1=evalðck Þ po p

k¼1

½1=evalðck Þ

for k ¼ 1; . . . ; N po p

(12)

where Npop represents the population size in a solution generation. It is obvious that highly fit chromosomes have a higher probability of being selected for mating in a reproduction step.

Fig. 18. Green-compliant optimal product structure of a different part combination.

C.-H. Chu et al. / Computers in Industry 60 (2009) 485–500 Table 13 Optimal product structure at lower recycling and recovery rates. Total cost

240.465 Dollars

Assembly method

(Barrel #7, Tip Barrel): Interference Fit (Cap, Clip): Adhesive Bonding (Tip Barrel, Cap): Adhesive Bonding (Cartridge, Tip Barrel): Adhesive Bonding Barrel #7 0.618279 0.618279

Disassembly sequence Recycling rate Recovery rate

This research adopts a crossover operator that randomly selects a bit in the encoding scheme to break two chromosomes and combine the gene sequence on one side of the bit in the first chromosome with that on the other side of the other one. Two new offspring chromosomes are thus reproduced. If a pair of chromosomes does not need to crossover, then chromosome cloning takes place. We use two crossover operations in the evolution process. The first one is designed for variation of the assembly sequence. A part is randomly selected from a parent chromosome and its assembly sequence is exchanged with that in the second parent chromosome. Fig. 12 shows that the second and third columns in the two chromosomes are going to switch and thus generate two new child solutions. Such a crossover operation may induce part duplication, e.g. there are two P1s in Child 1. A correction step needs to be applied to change the one not involved in the crossover, i.e. the first column in Child 1, to the part that is missing (P3 in this case). The corrected columns are circled in the figure. The second crossover is designed for variation of the assembly methods. The computation process is similar to the first crossover, i.e. two subassemblies are randomly picked from the parent

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chromosomes and their values are switched. Fig. 13 illustrates the result, in which the methods of assembling, A1 and A2, have been changed. In a GA algorithm, a mutation operator is designed to avoid the loss of genetic diversity and consequently to ensure that the evolution process is not trapped at a local optimum. Similar to the crossover operations, there are two mutation functions in our algorithm. The first one randomly selects a part, and a gene from the chromosome of the part. The gene value (a binary one) is then switched. The resultant variation of the product structure is shown in Fig. 14. The second mutation is performed to adjust the assembly method in a similar manner, as shown in Fig. 15. 5. Implementation and test results The proposed methods were implemented in CATIATM V5R15 using CAA V5TM (Component Applications Architecture) technologies and Microsoft’s Visual C++. CAA V5TM provides an integrated framework and a set of Applied Programming Interfaces (APIs) for the development of customized modules in CATIATM. Most geometric processing functions provided by the system and the proprietary data structure of the CATIA models are accessible via C++ function calls. Fig. 16 shows the implementation framework. It contains two major parts: a Green Design Module, a customized software component developed for this work, and the CATIA system. The Green Design Module consists of two computation mechanisms. Product structure variation is a VC++ static library that generates different product variants based on the three methods described in Section 2. A part database provides information for all the components to be used in the variation process through a text file. There is a CATIA part model

Table 14 Candidate parts that comprise the second test example.

Fig. 19. A non-optimal product structure of the second test example.

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corresponding to each of these components. The second part is responsible for the generation of a better product variant using GA. This C++ program is linked as a static library in the Green Design Module. One of the keys in the implementation is to access the CAD information and the geometric processing operations provided by the CATIA system. CAA V5TM was designed for this purpose. Customized modules can gain access to most of the computation resources that CATIA V5 is built upon, once they are implemented as a COM component in the CAA framework. Those functions are exposed as Direct Link Library (DLL) at runtime. In fact, most of the CAD functions in CATIA are implemented and integrated in this manner. The Green Design Module can thus inquire and obtain the design parameters of a part model and the assembly information of a product model. Likewise, an assembly operation can be carried out by applying the corresponding coordinate transformations to the parts involved in the assembly. We can estimate whether part interference occurs in the assembly operation. Third-party utilities (e.g. mathematical optimization library) can also be included in the CAA framework. A 3D ballpoint pen is used as a test example to demonstrate the effectiveness of this work. Table 7 shows the candidate parts that

comprise a pen and the design functions provided by each part. Table 8 describes all the design functions (F1 F2. . . F8) required by the pen. Other part attributes of concern include the part ID, material, and mass. These attribute values are retrieved from the corresponding CATIA model when a part is selected during the optimization process. The first step is to determine the part combinations that satisfy all the required design functions. This task is accomplished by a greedy search. For each part combination, the GA-based approach is applied to produce the resultant optimal product structures. The final design would be a better solution among the individual optimal architectures. Table 9 illustrates the unit costs for the different assembly and disassembly methods employed in constructing the product. Table 10 illustrates the optimal solution for each part combination and the corresponding costs. With the minimum recycling and recovery rates both set at 0.7, the optimal product structure of the four-part combination (Barrel #8, Tip Barrel, Cap, Cartridge) gives the minimum cost value. Table 11 shows the details of the results, which help designers to determine a better product structure while guaranteeing its compliance with the green directives and minimal total costs. Fig. 17 shows the corresponding assembly automatically generated in CATIA.

Fig. 20. Some product variants complying with the given rates.

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examples. The population size and the crossover rate are chosen as 50 and 0.6, respectively. They are set at the standard values for most GAs [22]. The mutation rate is 0.2 for the first 50 generations. If a better solution cannot be produced after 50 generations, the rate will be automatically increased to 0.3. The process will be terminated when the optimal solution remains the same for 150 generations. 6. Conclusions

Fig. 21. An optimal product structure for the second test example.

Another part combination (Barrel #7, Tip Barrel, Cap, Clip, Cartridge) is chosen to demonstrate the proposed part structure variation under various conditions. Fig. 18 shows the corresponding assembly. Table 12 summarizes the product structure generated with the recycling and recovery rates both set at 0.7. Note that only two parts (Barrel #7 and Cartridge), not all, need to be disassembled in order to satisfy the green directives. The total cost is slightly higher than the overall best value of 244.055 (see Table 11). The solution may change with different recycling and recovery rates. For instance, the total cost is reduced when the recycling and recovery rates are 0.6 (see Table 13). The number of parts to be disassembled is also reduced compared to the pervious result. Since Barrel#7 occupies a large portion of the product mass, it is the only part that needs to be taken apart. Interference fit is the chosen disassembly method for the part because of its lower operation cost. The other interfaces, without the need of disassembly, adopt the method of adhesive bonding because of its lower assembly cost. Obviously the green directives have a distinct effect on determining a better product design. A more complex product is included to demonstrate the effectiveness of the proposed method. Table 14 shows the components that can be chosen to form the product. A feasible product must fulfill six design functions (F1–F6). The assembly and disassembly costs are similar to those used in the previous example shown in Table 9. Fig. 19 illustrates a non-optimal solution with the recycling/recovery rates set at 0.736 and a total cost of 3141.75. The GA-based optimization process is then applied to reduce the cost, while the recycling/recovery rates must not be higher than 0.85. Fig. 20 shows some product variants generated during the optimization process. Fig. 21 lists the one with the lowest cost and its assembly sequence. The cost is reduced by 23% in this case. The parameter settings in GA remain the same in both

Reducing environmental impact has become an imperative for most developed countries and international enterprises. Companies have started to take special actions in product development in order to meet green directives, such as RoHS and WEEE, and achieve sustainable product development. Attempts undertaken at the design stage are considered to be the most effective and efficient. Among these, product structure design is a critical factor that not only characterizes how a product is formed, but to a large extent determines product end-of-life activities, like recycling strategies and the disassembly sequence. There have been numerous studies concerning the influences of product structure on these green issues and the assessment of its quantitative impact. However, most of these have assumed that the product structure is given as a priori and cannot be modified in their methodologies. Consequently, they have failed to leverage the vast latitude of the early design activities. Their results simply serve as an evaluation tool, without any feedback to product design. To overcome this deficiency, we proposed a CAD-based method that allows variation of product structure through changes in the part combination, assembly method, and assembly sequence. Design alternatives are thus generated. A computing scheme then produces an optimal product structure from among them using Genetic Algorithms. It gives the minimal assembly/disassembly costs while ensuring compliance with the recycling and recovery rates specified by the user. The assembly sequence and different assembly methods are encoded in binary chromosome representations with the costs as the fitness function. The optimization process determines a good product structure by defining the components comprising the product, the assembly method for each assembly process, and the assembly sequence. In addition, it chooses a small set of parts to be disassembled to meet with the green directives and suggests an economical disassembly process. The scheme has been implemented in a commercial CAD system. Two 3D product models were used to demonstrate the effectiveness of the scheme, which allows intelligent adjustment of product structure based on changes in the costs and the recycling and recovering rates, as well as limitations in part assembly. The results showed that the automatic variation of product structure is a simple but effective means of economical green product design. Future research may want to extend the idea of product structure variation to other activities in a product lifecycle, such as improving the efficiency of supply chain management and supplier selection. Since the current method adopts a simplified approach to product variation, more complex issues, like tolerance and the influence of different assembly methods on the product quality, should be studied to enhance the practicality of the approach. Acknowledgements This work was supported by the National Science Council of Taiwan under grant number NSC 95-2221-E-007-190-MY2. The authors would also like to thank Mr. Mu-Chi Sung at National Chia Tung University for his assistance in the system implementation.

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References [1] S.K. Das, P. Yedlarajiah, R. Narendra, An approach for estimating the end-of-life product disassembly effort and cost, International Journal of Production Research 38 (3) (2000) 657–673. [2] K.E. Moore, A. Gungor, S.M. Gupta, Petri net approach to disassembly process planning for products with complex AND/OR precedence relationships, European Journal of Operational Research 135 (2) (2001) 428–449. [3] A. Gungor, S.M. Gupta, Disassembly sequence plan generation using a branchand-bound algorithm, International Journal of Production Research 39 (3) (2001) 481–509. [4] T.C. Kuo, H.C. Zhang, S.H. Huang, Disassembly analysis for electromechanical products: a graph-based heuristic approach, International Journal of Production Research 38 (5) (2000) 993–1007. [5] T.C. Kuo, Disassembly sequence and cost analysis for electromechanical products, Robotics and Computer-Integrated Manufacturing 16 (1) (2000) 43–54. [6] A.J.D. Lambert, Exact methods in optimum disassembly sequence search for problems subject to sequence dependent costs, OMEGA - International Journal of Management Science 34 (6) (2006) 538–549. [7] B. Gonzalez, B. Adenso-Diaz, A bill of materials-based approach for end-of-life decision making in design for the environment, International Journal of Production Research 43 (10) (2005) 2071–2099. [8] K. Feldmann, S. Trautner, H. Lohrmann, K. Melzer, Computer-based product structure analysis for technical goods regarding optimal end-of-life strategies, Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture 215 (5) (2001) 683–693. [9] K. Feldmann, O. Meedt, S. Trautner, H. Scheller, W. Hoffman, The ‘‘Green Design Advisor’’: a tool for design for environment, Journal of Electronics Manufacturing 9 (1) (1999) 17–28. [10] H.E. Tseng, W.S. Chen, A replacement consideration for the end-of-life product in the green life cycle environment, International Journal of Advanced Manufacturing Technology 24 (11–12) (2004) 925–931. [11] S.F. Smith, Using multiple genetic operators to reduce premature convergence in genetic assembly planning, Computers in Industry 54 (2004) 35–49. [12] P.Y. Chao, T.T. Chen, Analysis of assembly through product configuration, Computers in Industry 44 (2001) 189–203. [13] R.B. Gottipolu, K. Ghosh, A simplified and efficient representation for evaluation and selection of assembly sequences, Computers in Industry 50 (2003) 251–264. [14] W. Van Holland, F. Bronsvoort, Assembly features in modeling and planning, Robotics and CIM 16 (4) (2000) 277–294. [15] K.T. Ulrich, S.D. Eppinger, Product Design and Development, McGraw Hill, 2000. [16] U. Roy, N. Pramanik, R. Sudarsan, R.D. Sriram, K.W. Lyons, Function-to-form mapping: model, representation and applications in design synthesis, Computer-Aided Design 33 (10) (2001) 699–719. [17] J. Hauser, D. Clausing, The house of quality, Harvard Business Review 66 (3) (1988) 63–73. [18] N.P. Suh, Axiomatic Design: Advances and Applications, Oxford University Press, 2001.

[19] K. Lee, Principles of CAD/CAM/CAE Systems, Addison-Wesley, 1999. [20] K. Lee, D.C. Gossard, A hierarchical data structure for representation assemblies. Part 1, Computer-Aided Design 17 (1) (1985) 15–19. [21] M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, 2002. [22] K.A. DeJong, W.M. Spears, An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms First Workshop Parallel Problem Solving from Nature, Springer-Verlag, Berlin, 1990, pp. 38–47.

Chih-Hsing Chu attended National Taiwan University and received his B.S. and M.S. from Department of Mechanical Engineering. He received his Ph.D. in mechanical engineering at the Laboratory for Manufacturing Automation, University of California at Berkeley, USA. His past work experiences include a web applications engineer at RedSpark, an AutodeskTM Venture, USA, a research intern at DaimlerBenzTM AG, Germany, and a visiting researcher at the Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen, Germany. Prior to joining National Tsing Hua University in 2002, he was on the faculty of Industrial and Systems Engineering Department, Virginia Tech, USA. He was an invited scholar at CREDITS Center, Sungkunkwan University, Korea, during the summer of 2005. Dr. Chu has published more than 100 research papers. He is on the editorial board of IEEE Transactions on Automation Science and Engineering (IEEETASE), Journal of the Chinese Institute of Industrial Engineers (JCIIE), and International Journal of Electronic Business Management (IJEBM). His research interests include collaborative design, product development, design chain management, and CAD/CAM/PLM.

Yuan-Ping Luh is an assistant professor in Institute of Manufacturing Technology at National Taipei University of Technology, Taiwan, R.O.C. He received his Ph.D. degree from Cornell University in 1996. He was an eBusiness consultant from 1997 to 2002. He started his academic career since then. His current research focuses on the information and communication technologies (ICT) and management applications for global manufacturing industry, including collaborative system development, RFID system development, product data management, supply chain management, product lifecycle management, collaborative product commerce, and global logistics management. Currently, he also serves as the deputy director of RFID project office at Ministry of Education in Taiwan.