Optimization of process plans using a constraint-based tabu search approach

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<ul><li><p>This article was downloaded by: [University of California, San Francisco]On: 19 November 2014, At: 04:33Publisher: Taylor &amp; FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK</p><p>International Journal of ProductionResearchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tprs20</p><p>Optimization of process plans usinga constraint-based tabu searchapproachW. D. Li a , S. K. Ong b &amp; A. Y. C. Nee ba Singapore Institute of Manufacturing Technology , 71Nanyang Drive, Singapore 638075b Department of Mechanical Engineering , National Universityof Singapore , 9 Engineering Drive 1, Singapore 117576Published online: 21 Feb 2007.</p><p>To cite this article: W. D. Li , S. K. Ong &amp; A. Y. C. Nee (2004) Optimization of process plansusing a constraint-based tabu search approach, International Journal of Production Research,42:10, 1955-1985</p><p>To link to this article: http://dx.doi.org/10.1080/00207540310001652897</p><p>PLEASE SCROLL DOWN FOR ARTICLE</p><p>Taylor &amp; Francis makes every effort to ensure the accuracy of all the information (theContent) contained in the publications on our platform. However, Taylor &amp; Francis,our agents, and our licensors make no representations or warranties whatsoeveras to the accuracy, completeness, or suitability for any purpose of the Content. Anyopinions and views expressed in this publication are the opinions and views of theauthors, and are not the views of or endorsed by Taylor &amp; Francis. The accuracyof the Content should not be relied upon and should be independently verifiedwith primary sources of information. Taylor and Francis shall not be liable for anylosses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connectionwith, in relation to or arising out of the use of the Content.</p><p>This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden.</p><p>http://www.tandfonline.com/loi/tprs20http://dx.doi.org/10.1080/00207540310001652897</p></li><li><p>Terms &amp; Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f C</p><p>alif</p><p>orni</p><p>a, S</p><p>an F</p><p>ranc</p><p>isco</p><p>] at</p><p> 04:</p><p>33 1</p><p>9 N</p><p>ovem</p><p>ber </p><p>2014</p><p>http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditions</p></li><li><p>int. j. prod. res., 15 may 2004, vol. 42, no. 10, 19551985</p><p>Optimization of process plans using a constraint-based tabu searchapproach</p><p>W. D. LIy, S. K. ONGz and A. Y. C. NEEz*</p><p>A computer-aided process planning system should ideally generate and opti-mize process plans to ensure the application of good manufacturing practicesand maintain the consistency of the desired functional specifications of a partduring its production processes. Crucial processes, such as selecting machiningresources, determining set-up plans and sequencing operations of a part shouldbe considered simultaneously to achieve global optimal solutions. In this paper,these processes are integrated and modelled as a constraint-based optimizationproblem, and a tabu search-based approach is proposed to solve it effectively. Inthe optimization model, costs of the utilized machines and cutting tools, machinechanges, tool changes, set-ups and departure from good manufacturing practices(penalty function) are the optimization evaluation criteria. Precedence constraintsfrom the geometric and manufacturing interactions between features and theirrelated operations in a part are defined and classified according to their effects onthe plan feasibility and processing quality. A hybrid constraint-handling methodis developed and embedded in the optimization algorithm to conduct the searchefficiently in a large-size constraint-based space. Case studies, which are used forcomparing this approach with the genetic algorithm and simulated annealingapproaches, and the proposed constraint-handling method and other constraintmethods, are discussed to highlight the performance of this approach in terms ofthe solution quality and computational efficiency of the algorithm.</p><p>1. IntroductionComputer-aided process planning (CAPP) is an essential component for linking</p><p>the various models and processes in a computer-integrated manufacturing system(CIMS). Process plans generated by a CAPP system should ensure the consistencyof the desired functional specifications of a part during its production processesand the observation of good practices to manufacture the part economically andcompetitively. The initial variant CAPP systems are based on the group technology(GT) coding and classification system to select a baseline process plan for a partfamily. In such systems, approximately 90% of the part attributes and parameterscan be yielded automatically while the remaining 10% is achieved through modifyingor fine-turning the process plans manually. Current systems are mostly generativeand artificial intelligence (AI) techniques are used to facilitate the generation of acomplete process plan. An important trend in generative CAPP systems is to makethem more adaptable to dynamically varying manufacturing conditions such asthe availability of machines and tools. Conventionally, a generative CAPP system</p><p>Revision received September 2003.ySingapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075.zDepartment of Mechanical Engineering, National University of Singapore, 9</p><p>Engineering Drive 1, Singapore 117576.*To whom correspondence should be addressed. e-mail: mpeneeyc@nus.edu.sg</p><p>International Journal of Production Research ISSN 00207543 print/ISSN 1366588X online # 2004 Taylor &amp; Francis Ltd</p><p>http://www.tandf.co.uk/journals</p><p>DOI: 10.1080/00207540310001652897</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f C</p><p>alif</p><p>orni</p><p>a, S</p><p>an F</p><p>ranc</p><p>isco</p><p>] at</p><p> 04:</p><p>33 1</p><p>9 N</p><p>ovem</p><p>ber </p><p>2014</p></li><li><p>consists of three main consecutive activities: (1) recognizing manufacturing features</p><p>from a designed part; (2) determining machining operation types and enumerating</p><p>alternative set-up plans as well as applicable machining resources in a dynamic</p><p>workshop environment; and (3) selecting suitable set-up plans and machining</p><p>resources, and sequencing machining operations to seek the lowest machining cost</p><p>of the part. The workflow of a generative CAPP system is shown in figure 1. A global</p><p>optimum process plan can be achieved from an optimization of each individual</p><p>activity. The third activity, which is the focus of this paper, can be modelled as an</p><p>optimization problem and solved using AI techniques. However, such a problem is</p><p>well known to be an intractable reasoning and decision-making process considering</p><p>the inter-related geometric relationships between features, the complex technologi-</p><p>cal requirements and the multiple evaluation criteria. To address this problem</p><p>effectively, effort should be made to design a more apt optimization model and</p><p>develop a more efficient method for handling precedence constraints of a part.</p><p>Determination of machining operations for the manufacturing features </p><p>Selection of suitable set-ups and machining resources </p><p>Sequencing machining operations considering the precedence constraints </p><p>Machining resources</p><p>AI reasoningengines </p><p>Enumeration of candidate set-up plans and applicable machining resources for </p><p>the machining operations </p><p>Determination of precedence constraints among the machining operations</p><p>Optimizationalgorithms </p><p>A designed part Recognized</p><p>manufacturing features</p><p>Geometric and AIreasoning engines </p><p>Phase 1:Recognition ofmanufacturingfeatures</p><p>Phase 2: Identification of machiningoperations and resources</p><p>Phase 3:Determination ofprocess plans and machining cost</p><p>Optimized process plansand machining cost </p><p>Figure 1. The workflow of a generative CAPP system.</p><p>1956 W.D. Li et al.</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f C</p><p>alif</p><p>orni</p><p>a, S</p><p>an F</p><p>ranc</p><p>isco</p><p>] at</p><p> 04:</p><p>33 1</p><p>9 N</p><p>ovem</p><p>ber </p><p>2014</p></li><li><p>In this paper, selecting machining resources, determining set-up plans andsequencing operations for the process planning of a prismatic part are incorporatedas a constraint-based optimization model. In this model, six criteria are evaluated:(1) cost of machines utilization; (2) cost of cutting tools utilization; (3) number ofmachine changes; (4) number of tool changes; (5) number of set-ups; and (6) numberof violated constraints (a penalty function). Some precedence constraints based onthe geometric and manufacturing interactions between features and their relatedoperations in a part are defined and classified from the viewpoints of plan feasibilityand processing quality. A tabu search-based approach embedded with a hybridconstraint-handling method is developed to effectively search for optimized processplans in a large-size multiple-dimensional space with complex constraints. Throughcomprehensive comparisons of this approach with a genetic algorithm and asimulated annealing approach for two prismatic parts under different workingconditions, and the proposed constraint-handling method in this research withother constraint methods, the merits can be illustrated clearly.</p><p>The rest of this paper consists of four sections. In section 2, previous related workis investigated and summarized. In section 3, representation models of process plans,precedence constraints and optimization criteria are defined. Based on these models,a constraint-based tabu search approach is described in detail. In section 4, compu-tational experiments are made to compare the approach with other methods. Finally,the present study is concluded and future research is outlined in section 5.</p><p>2. Previous related workRecent related work can be categorized as the knowledge-based reasoning</p><p>approach (Chang 1990, Wong and Siu 1995, Chu and Gadh 1996, Wu and Chang1998, Tseng and Liu 2001), graph manipulation approach (Chen and LeClair 1994,Irani et al. 1995, Lin et al. 1998), Petri-nets-based approach (Kruth and Detand1992), fuzzy logic reasoning approach (Ong and Nee 1994, Zhang and Huang1994, Gu et al. 1997), evolutional algorithm and heuristic reasoning-based approach(Vancza and Markus 1991, Chen and LeClair 1994, Yip-Hoi and Dutta 1996, Zhanget al. 1997, Chen et al. 1998, Reddy et al. 1999, Ma et al. 2000, Qiao et al. 2000, Leeet al. 2001, Li et al. 2002a), etc. In the reasoning processes, manipulating preliminaryprecedence constraints between operations effectively using if-then rules, graphsand matrices, is an important issue.</p><p>The QTC system developed by Chang (1990) uses a knowledge-based reasoningapproach, and aggregates machining operations with the same tool approachdirection (TAD) as a set-up. The sequence of the machining operations andset-ups is reasoned according to the precedence constraints, and an optimal sequenceis selected from several feasible sequences based on the minimum number of set-ups.In this system, several geometric and technological constraints of a part, stemmingfrom geometric interactions between operations, location tolerance requirements,reference or datum requirements, and good manufacturing practices, are consideredin the reasoning process. The aggregation concept was enhanced by Chu and Gadh(1996) to cluster operations that are machined with the same cutting tool in a set-upso as to reduce the number of tool changes. In the APSS system reported byWong and Siu (1995), the operations sequencing algorithm consists of threeconsecutive algorithms, viz., the transformation algorithm, the refinement algorithm,and the linearization algorithm. After preliminary precedence constraints have beengenerated in the transformation algorithm, the refinement algorithm creates the</p><p>1957Optimization of process plans</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f C</p><p>alif</p><p>orni</p><p>a, S</p><p>an F</p><p>ranc</p><p>isco</p><p>] at</p><p> 04:</p><p>33 1</p><p>9 N</p><p>ovem</p><p>ber </p><p>2014</p></li><li><p>details of the operations using a refinement knowledge base considering goodmanufacturing practices, and represents them in a tree structure. In the linearizationalgorithm, the tree structure will be linearized into the final required operationsequence.</p><p>Irani et al. (1995) developed a graph manipulation approach for operationssequencing, in which the Hamiltonian path (HP) analogy is utilized to representthe process plans and the Latin multiplication method (LMM) is implemented toenumerate all the feasible HPs under constraints. The optimal process plan is an HPthat corresponds to the least number of set-up disruptions required from start tofinish to process each feature once and only once. In the work reported by Lin et al.(1998), a graph-search strategy was designed for operations sequence planning forprismatic parts with interacting features. The machining of a feature might affect thesurface quality of other interacting features, and several heuristic rules concerningmachining practices were developed to help the graph-search process generatehigh-quality process plans with lowest machining cost. A hybrid method combiningan unsupervised learning algorithm and a graph manipulation algorithm wasproposed by Chen and LeClair (1994). The sequence of the features in each set-upis constrained and determined by the interacting feature relationships, which arestored in an episodal associative memory. After the process of feature sequencing,several graph manipulation algorithms are proposed to obtain the optimal toolssequence for creating the features in a set-up by minimizing the tool changes.</p><p>Kruth and Detand (1992) proposed generic Petri-nets to represent parametricfeatures and their related machining operations. After being evaluated usingmanufacturing knowledge bases, such as general machine data and manufacturingcapability data, the separated Petri-nets for compound features or features withidentical TADs are joined together. The same procedure is then applied to thefeatures with different TADs, and a large Petri-net is finally formed, in which allvalid alternatives to machine a part are described.</p><p>The common characteristics of the above approaches include: (1) difficulty infinding global and optimal plans using these reasoning approaches although theseapproaches can generate feasible solutions; and (2) low reasoning efficiency in acomplex machining environment.</p><p>Process planning objectives are often imprecise and can even be conflicting due toinherent differences in the feature geometry and technological requirements. Fuzzylogic is suitable for presenting such imprecise knowledge and several approachesusing this technique have been developed to address process planning problems.Based on fuzzy memberships, the objective (Zhang and Huang 1994, Tiwari andVidyarthi 1998) is to minimize the dissimilarity among the process plans selected fora family of parts, and optimal process plans can be generated for each part family. Afuzzy logic-based approach has been reported by O...</p></li></ul>


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