The international journal of advanced manufacturing technology, tập 59, số 5 8, 2012

Int J Adv Manuf Technol (2012) 59:421–432 DOI 10.1007/s00170-011-3516-y ORIGINAL ARTICLE A model to build manufacturing process chains during embodiment design phases Robert Blanch & Ines Ferrer & Maria Luisa Garcia-Romeu Received: February 2011 / Accepted: July 2011 / Published online: 22 July 2011 # Springer-Verlag London Limited 2011 Abstract The methods for manufacturing process selection from early design phases avoid later mistakes and ensure the success during product manufacturing Currently, the majority of the products need more than one manufacturing process to become finished parts This is known as a manufacturing processes chain, and it is important that this manufacturing chain is well designed This paper presents the bases and the activity model (IDEFØ) to develop a decision-support system that helps designers and manufacturing engineers to configure manufacturing process chains while the product is being designed The model schematizes all the activities and information involved in obtaining reliable manufacturing process chains The support system has been applied to an air-bending die design process to be used to perform either air-bending or bottoming Keywords Manufacturing process Process selection Activity model Decision-support system Introduction In a context of profound changes in industrial markets—in relation to globalization and delocalization—the main R Blanch : I Ferrer (*) : M L Garcia-Romeu Department of Mechanical Engineering and Industrial Construction, University of Girona, Campus Montilivi P-II, Girona 17071, Spain e-mail: ines.iferrer@udg.edu R Blanch e-mail: robert.blanch@udg.edu M L Garcia-Romeu e-mail: mluisa.gromeu@udg.edu challenge for all industries is to remain competitive [1] In this context, companies need to focus on satisfying as much as possible the product requirements demanded by the market During the first stage of product development—the design process—many decisions are made to meet these requirements; however, such decisions also affect on other issues, such as process planning, manufacturing, assembly or recycling of the product Considering these issues during the design stage is important because wrong decisions can have serious effects on development time, cost, and product quality [2, 3] Given that manufacturing issues must be taken into account at the initial stages of design [3, 4], the designer should know the manufacturing processes or sequence of processes (i.e., the manufacturing process chain) that may be used to manufacture what they are designing This, however, is not an easy task First, there is a large variety of manufacturing processes; second, the knowledge associated with each process is abundant; and, finally, the increasing trend towards relocating and separating manufacturing and design centers from each other has led to a decline in designers’ understanding of manufacturing processes by making them less accessible To solve this problem, several methods and tools have been developed to help designers select suitable manufacturing processes during product design Manufacturing process selection tools help designers choose the most technically and economically suitable manufacturing process to obtain a product [3, 5] Most of the work developed is based on quantitative analysis In manufacturing process selection-based on quantitative analysis (MPS-BQA), the choice is made by comparing the design parameters or specifications with the attributes of the manufacturing process Process attributes describe the capabilities of the process in terms of material, shape, size, 422 tolerances, production rate, cost, and environmental impact, allowing direct, objective comparisons to be made [5], for example, of the tolerance or roughness each process is able to obtain in a part Some relevant examples of these research studies are: CES [6], MAS 2.0 [7], WiSeProM [8], and WebMCSS [9] These tools may be applied from the preliminary design stages, in which there is already a rough idea of design parameters, such as shape, material and weight, as well as of product restrictions, such as production volume or cost limit The tools result is a list of manufacturing processes which are able to achieve the basic product form but designer have to chose only one manufacturing process option (a, b, c, d, and e in Fig 1) without combining more than one process as a chain derivation allows To obtain manufacturing process chain, two basic requirements have to be considered First, how much and in what way a product is modified during each process in the manufacturing process chain needs to be considered, thus revealing what remains to be done in the following processes Second, the compatibility of different manufacturing processes needs to be considered to develop manufacturing process chains that are technically feasible This means ensuring that a particular process is compatible with the subsequent process The process chain can be defined from early design using a selection process or during detail design using a configuration process (Fig 1) The manufacturing process chain selection comprises all the manufacturing processes— taken as a sequence of processes—that meet all the product requirements [10] For example, chains I and II in Fig On the other hand, configuring the manufacturing process chain means choosing the machinery, tools, and other production parameters that will meet the product quality Fig Manufacturing process chain related to the design process Int J Adv Manuf Technol (2012) 59:421–432 requirements [10] (see chains III and IV in Fig 1.) Therefore, the configuration takes place at the process planning level This research is intended to develop a decision-support system to help designers or manufacturer engineers know the sets of manufacturing process chains that could be used to manufacture the products being designed during early design It is assumed that each chain is able to manufacture the product in its entirety However, the paper presents the first stages in the development of this system First of all, the framework approach on which the system is based is described Second, the IDEFØ activity model, in which all the activities, information, and knowledge involved in obtaining a set of viable manufacturing process chains are gathered, is presented to help select manufacturing process chains Finally, an example that shows the application of the model is explained in detail There are three main advantages of such a method First, the design parameters are better adapted to the manufacturing requirements and there is a better validation of the manufacturability of the design for all the processes involved in its manufacture; second, any problems during the manufacturing phase arising from an unsuitable design are reduced since these problems are detected during the design process; and, finally, production costs can be calculated and compared for various manufacturing process chains Framework approach The manufacturing process chain is defined as a process map that describes how the initial product blank is Int J Adv Manuf Technol (2012) 59:421–432 transformed into the final product To get a manufacturing process chain capable of producing a product, a manufacturing process chain derivation method is used, which is the core of the method described in this research study (Fig 2) The derivation method which will be presented next is based on both design information and the capabilities of the processes for transforming the products, and provides as a result the set of viable manufacturing process chains that will produce the product It is focused on mechanical products As shown in Fig 2, the manufacturing process chain must begin to take shape during the embodiment design phase [9, 11], when the requirements and the functionality are defined, and a preliminary draft is written All this design information has to be compiled in the product design parameters, which are a qualitative description of the designed product Basically, these parameters have been extracted from research works related to MPS-BQA [6, 7, 12], but they have been classified into three lists: required, optional, and feature design parameters, which are explained in detail in section The manufacturing process chain derivation method requires concise information about the manufacturing Fig Manufacturing process chain derivation 423 processes, especially regarding their capacity to modify the product with respect to the design parameters This information has to be comparable with the product information in order to create viable manufacturing process chains from a technological point of view The manufacturing process description is divided into three parts (Fig 2): & The manufacturing process information concerns manufacturing process data related to product design and is divided into manufacturing process constraints and manufacturing process transformation capabilities – The manufacturing process constraints are attributes that describe the manufacturing processes and their ability to meet the product design parameters These constraints include process capabilities related to material, shape, geometrical dimensions (e.g., thickness or tolerance), roughness, geometrical features, and production rates, which also define the product, allowing direct and objective comparisons to be made between design and manufacturing information They are, therefore, the same as process attributes defined by Lovatt and Shercliff [5] 424 Int J Adv Manuf Technol (2012) 59:421–432 – & & The manufacturing process transformation capabilities represent the capability of each manufacturing process to modify the product design parameters from the initial stage or to modify the product design parameters that have been modified by previous manufacturing processes These capabilities are defined using maximum values of transformation, which quantify how much a manufacturing process can change a product parameter Furthermore, the differences regarding manufacturing process constraints will be discussed further The manufacturing process sequencing rules define technological constraints among different manufacturing processes so that it is possible to distinguish between viable and non-viable manufacturing process chains, because not all process combinations are viable as a manufacturing process chain [11] Therefore, for each manufacturing process, it needs to specify all the other compatible manufacturing processes that can be carried out before it, after it, or both (Fig 3) Figure shows an example of the sequencing rules for the milling process It shows that during the manufacturing of a part, the processes of casting and powder metallurgy must always take place before milling, whereas bending or drilling processes (labeled “both” in the figure) can take place either before or after milling The polishing process, however, must take place after milling The manufacturing process classification classifies manufacturing processes that vary according to the objective pursued with this classification The manufacturing process classification proposed by Lovatt and Shercliff [5] is used in this work The processes are classified according to the extent to which they can transform the part and are classified as [5]: primary, secondary, and tertiary The “primary processes” take unshaped material (liquid metal, or a powder, or a solid ingot) and give it shape Thus, molding, casting or machining processes are primary The “secondary processes” modify, add, or refine features to an already-shaped body, such as fine machining and Fig Example of milling process sequencing rules polishing And finally, the “tertiary processes” add quality either to the bulk or to the surface of a component, for example, shot-peening of surfaces Although this classification is not absolute, since a particular process, such as machining, may belong to more than one group, the use of this process classification reduces the complexity of the problem and limits the number of candidate processes for manufacturing at each level of the product design Therefore, it limits the number of processes that need to be analyzed in order to configure each step of the manufacturing process chain Process chain derivation model Modeling knowledge and information used to integrate design information with manufacturing information has been extensively studied and is still a very active field, as confirmed by the following studies Skander et al [1] modeled all the product information, the manufacturing constraints related to design, and the required rules to implement a method that integrated process selection and manufacturing constraints into the design Ferrer et al [13] proposed a method to formalize the most relevant design information related to manufacturing that should be made available to the designer to design for manufacturing of new designs Ciurana et al [14] modeled the process planning activities in sheet metal processes and the model was implemented in a computer-aided tool Guerra-Zubiaga and Young [15] show different ways to model manufacturing knowledge and how to make it available when needed Thibault et al [16] propose an integrated product–process approach to evaluate its consistency and is useful in selecting suitable forging process and product design parameters Yuh-Jen Chen [17] modeled the process for conventional molding product design and process development by using the process modeling technique IDEFØ And finally, Mauchanda et al [18] model the knowledge and information to develop a tool to calculate the manufacturing cost from conceptual design Int J Adv Manuf Technol (2012) 59:421–432 In accordance with the framework approach presented in Section 2, an activities model using IDEFØ methodology has been developed as skeleton of a decision-support system to obtain a process chain The purpose is to schematize all the activities involved in obtaining the viable manufacturing process chains to manufacture a given design from the designer’s point of view, i.e., to derive the process chain (Fig 4) The inputs required to carry out the main activity are the computer-aided design (CAD) part sketch and the manufacturing process pool, whereas the output will be the set of viable manufacturing process chains Manufacturing process information, manufacturing process sequencing rules, and manufacturing process classification act as controls The manufacturing process pool represents the whole set of manufacturing processes that are considered for the selection It may be wider or narrower depending on the scope This main activity, A0, is broken down into four specific activities, A1, A2, A3, and A4, shown in Fig 4, which will now be described in detail Activity A1 “Analyze the product” In this activity, the designer has to analyze the product information from the CAD part sketch and classify it into three lists of design parameters (see Fig 5): required, optional, and feature design parameters In this way, the design parameters are organized in terms of how they can be obtained by the manufacturing processes that will form part of the chain, which is important to establish process chains The first list consists of the required design parameters, which are those that all the manufacturing processes in the process chain have to be able to deal with These parameters are exclusive, which means that a process is excluded when it is not able to process with this property, for any step of the process chain The second list is the optional design parameters, which are Fig The basic derivation of the process chain A0 425 product parameters that may be transformed by various manufacturing processes until the final optional design parameter is reached Finally, the third list is the feature design parameters, where a feature refers to the significant processing of portions of the geometric shape of a part or assembly Neither optional nor features are exclusive because they can be obtained along the process chain Activity A2 “Analyze and select manufacturing process level 1” The goal of this activity is to analyze and select the first manufacturing process in the process chain from the manufacturing process pool, using the required, optional and feature design parameters as inputs, and both manufacturing process information and manufacturing process classification as controls (Fig 5) Two outputs are obtained: a set of manufacturing processes ranked according to which should occupy the first position of the process chain, called manufacturing process ranking for level 1, and a list of resolved/unresolved design parameters The resolved design parameters are those which will have been completely transformed or changed by the selected process whereas the unresolved design parameters are those which will require further manufacturing processes Activity A2 is further broken down into four sub-activities, shown in Fig A2.1 “Select manufacturing processes compatible with the material” The inputs for this sub-activity are the manufacturing process pool and the material design parameter The material of the product is compared to the set of materials with which 426 Int J Adv Manuf Technol (2012) 59:421–432 Fig Detailed derivation of the process chain each process is able to work, thus the result obtained is a list of manufacturing processes compatible with the material The material for required design parameters was chosen as the first discriminatory step because this parameter is the most restrictive in terms of selecting manufacturing processes and it reduces the search range for the next steps [5, 9] It means that choosing the material for the first step a lot of processes can be excluded since the product cannot be obtained A2.2 “Check required parameters” This sub-activity checks whether or not the processes in the list of manufacturing processes compatible with the material (from activity A2.1) are able to manufacture the other required design parameters These parameters are compared to the manufacturing process constraints of each process When the process is able to obtain all the parameters from the list of required design parameters then the process is kept on the list; otherwise it is excluded The result is the list of manufacturing processes satisfying required properties A2.3 “Check optional and feature parameters” In this activity the lists of optional and feature design parameters are checked The result is the viable manufacturing process list and a first version of the list of resolved/ unresolved design parameters indicating which processes are able to transform the part according to those parameters and which ones are not A2.4 “Evaluate the manufacturing process transformation” As stated in Section 2, transformation is the capability of each manufacturing process to modify the parameters of the product either from the initial stage or after a previous manufacturing process has already modified them It means that achieving the values of a given parameter depends on the starting value of this parameter on the part To evaluate the manufacturing process transformation, the method needs to calculate the transformation required in the product parameters by comparing the status of these parameters from one manufacturing process to the next Subsequently, the values obtained for the required product transformation must be compared with the transformation capabilities of the particular manufacturing process When the calculated values are less than or equal to the manufacturing process transformation capabilities, the manufacturing process is deemed able to transform all the “resolved design parameters” of the part and therefore there is no need to update the list of resolved/unresolved design parameters Otherwise, when the calculated values are greater than the manufacturing process transformation capability, the list of resolved/unresolved design parameters will be updated accordingly A2.5 “Estimate the manufacturing cost” In the fifth and last sub-activity of A2, the viable manufacturing processes are ranked Int J Adv Manuf Technol (2012) 59:421–432 427 Fig Details of Activity A2—analysis and selection of manufacturing process level according to economical criterion Several methods have been developed for manufacturing cost estimation from early design stages, for example CES [6] and Swift and Booker [19] method These methods are based on three main elements: material and consumables, tooling and equipments, and investment, where the batch size becomes a key factor Depending on the value of the batch size the manufacturing cost changes considerably In addition, some processes that may be viable from a technological point of view become non-viable from an economical point of view depending on the batch size When A2 activity is complete, it might be that a single manufacturing process can make the entire part or, in contrast, that it is necessary to continue building the chain of manufacturing processes This decision is determined by the list of resolved/unresolved design parameters If all design parameters are resolved, the chain of manufacturing processes is complete and activity A4 will be implemented, showing the first result If they are “unresolved” and there are still some parameters that have not been achieved or only partly achieved, activity A3 continues the elaboration of the chain of manufacturing processes until all the design parameters are resolved Activity A3 “Analyze and select process level n (A3)” In this activity, the manufacturing process ranking for level from the activity (A2) is used to evaluate new manufacturing processes for the next step in the process chain 428 Int J Adv Manuf Technol (2012) 59:421–432 In addition, a new control is used: manufacturing process sequencing rules These rules validate the technological feasibility of each combination of manufacturing processes Although the procedure of this activity is similar to that of the previous activity (A2), there are two main differences The first change is the starting point, since now it has the list of resolved and unresolved parameters from the previous activity, representing the design properties carried out by the previous process and those pending in the next one This list will be updated until the manufacturing process chain resolves all the unresolved parameters The second difference is that the transformation calculation is carried out using the lists of resolved and unresolved parameters from the Fig CAD sketch of the die used in the case study previous manufacturing process as well as the process currently being checked Activity A4 “View final process chain (A4)” This activity provides a list detailing the selected manufacturing processes that make up the process chain Application of the proposed model The developed model was applied to a selected set of mechanical parts However, in this work the design process of an air-bending die (Fig 7) to be used to perform either air-bending or bottoming is discussed in detail The manufacturing processes are reduced in this sample to “powder metallurgy”, “machining”, “polishing”, “hot closed die forging”, and “roll forming” Nevertheless, the Int J Adv Manuf Technol (2012) 59:421–432 429 model developed is also applicable for other mechanical parts than this sample and whole manufacturing processes feasible for mechanical parts being manufactured Following the proposed IDEFØ diagram and based on the current version of the “CAD part sketch” (Fig 7), the designer or manufacturing engineer has to extract the design parameters and classify them into required, optional and feature design parameters Table shows these three lists and the values of the parameters for the case study The lists are produced during activity A1, as shown in Fig In activity A2 (Fig 5), the lists of product design parameters from Table and the manufacturing process pool are used to produce two outputs The first one is the list of processes that can be used as the first manufacturing process of the process chain which will initiate production of the part, i.e., “hot closed die forging”, “powder metallurgy”, and “machining” The second output is the list of resolved/ unresolved parameters, which it will be explained later Nevertheless, to achieve these outputs, the A2 sub-activities must first be carried out Figure shows in detail the results of these A2 sub-activities for the die case study Initially, the A2.1 sub-activity gives a list of all the manufacturing processes capable of working with the material of the product in question, comparing the product material with the set of materials that each process is able to manufacture “Hot closed die forging”, “powder metallurgy”, “machining”, and “roll-forming” make up the list of manufacturing processes compatible with the Table Product design parameters of the case study Product design parameters List Parameter Value Required design parameters Material Iron Shape Length (X) Width (Y) Prismatic–nonaxisymmetric–solid [75; 75] mm [115; 115] mm Height (Z) Weight General roughness Specific roughness General tolerance Specific tolerance Corner radius Type Diameter Height (Z) Roughness Tolerance [24; 55] mm kg 10 μm μm 10 0.5 Hole 8.5 mm 55 mm 0.1 μm 0.002 Optional design parameters Feature design parameters material Subsequently, these processes are further filtered by sub-activities A2.2 and A2.3 Activity A2.2 checks the list of manufacturing processes compatible with the material to see which ones satisfy the other required parameters, which in the example are weight and height Both are numeric parameters and it is checked that its value is included in the range of values that each process is able to achieve, according to its manufacturing process constraint The “hot closed die forging”, “powder metallurgy”, and “machining” processes meet these requirements and are therefore allowed to continue as input for the next activity, A2.3, In contrast, the “roll forming” process cannot achieve the required height and is removed from the list Now, activity A2.3 checks the list to see if these processes are capable of manufacturing the optional and feature design parameters, which in this case include general roughness, specific roughness, and hole As shown in Fig 8, the process “hot closed die forging” can meet the material, weight, general roughness and height requirements, but not the specific roughness and hole requirements Choosing this process would require a subsequent manufacturing process to complete the part In contrast, “machining” is able to resolve all the design parameters, which suggests that, for this case study, this process would be sufficient to produce the part However, following the model proposed here, it is necessary to analyze whether each process can transform the objectives set out in the list of design parameters (sub-activity A2.4) At this point, the method has evaluated the capacity of the processes to meet the product design parameters taking into account the manufacturing process constraints However, activity A2.4 assesses the capability of the manufacturing processes to transform the parameters from the output list of activity A2.3 Figure shows the results of activity A2.4 for the process “hot closed die forging” Fig The process “hot closed die forging” has to transform the parameters of weight, height, and general roughness from an initial status (previous step) to a final status (next step) In that case, the initial status corresponds to the material blank, which is considered as the volumetric space of the part Therefore, the values for weight and height take it into account The final values of these parameters appear in the next step The parameters are quantified with a numerical value—as the weight—or using a range that shows the maximum and minimum values the parameter takes in the part—as the height dimension The result of this transformation is described in the product transformation needed column in the product transformation table The resulting values must then be compared with the range of transformation values found for “hot closed die forging” in the list of manufacturing process transformation capabilities The Int J Adv Manuf Technol (2012) 59:815–828 Table Computational results of experiment No 825 TO GA SA Best Worst Mean Best Worst Mean Best Worst Mean 14 197.0 187.0 197.0 192.0 197.0 187.4 197.0 187.0 197.0 187.0 197.0 187.0 197.0 187.0 197.0 187.0 197.0 187.0 19 198.0 321.0 237.6 163.0 231.0 194.0 163.0 193.0 178.6 16 18 269.0 124.0 293.0 159.0 270.2 142.6 269.0 124.0 269.0 154.0 269.0 139.2 269.0 124.0 269.0 124.0 269.0 124.0 20 220.0 280.0 241.8 190.0 250.0 220.7 190.0 190.0 190.0 21 20 206.0 148.0 236.0 148.0 210.8 148.0 206.0 148.0 236.0 148.0 213.5 148.0 206.0 148.0 206.0 148.0 206.0 148.0 20 164.0 227.0 189.4 140.0 198.0 166.4 140.0 164.0 142.5 10 11 137.0 137.0 137.0 137.0 137.0 137.0 137.0 137.0 137.0 11 12 18 119.0 307.0 179.0 367.0 141.5 333.5 119.0 307.0 149.0 337.0 125.0 323.5 119.0 307.0 119.0 307.0 119.0 307.0 13 18 133.0 163.0 137.7 133.0 163.0 140.5 133.0 133.0 133.0 14 15 13 15 127.0 156.0 157.0 216.0 131.5 180.0 127.0 156.0 128.0 186.0 127.1 159.0 127.0 156.0 127.0 156.0 127.0 156.0 16 17 18 21 22 17 147.0 167.0 182.0 164.0 197.0 242.0 149.5 173.2 209.3 147.0 167.0 182.0 164.0 222.0 212.0 148.7 169.7 197.5 147.0 167.0 182.0 147.0 167.0 182.0 147.0 167.0 182.0 another flexibility, that is, tool flexibility, is taken into consideration compared with experiment Computational results in Table confirm the proposed ICA’s effectiveness in solving process planning with various flexibilities Table Computational results of experiment ICA No TO 5.4 Experiment Experiment consists of five problems with various numbers of operations In this experiment, the impact of GA SA ICA Best Worst Mean Best Worst Mean Best Worst Mean 16 20 18 20 15 627 413 667 657 417 717 488 798 759 512 648.0 450.6 738.1 717.9 461.9 630.0 409.0 604 617 371 663.0 487.0 759 709 451 640.5 440.3 650.4 668.6 398.1 627 409 603 613 371 631 428 638 660 379 629.2 417.4 648.8 645.6 376.8 10 11 12 13 14 15 16 17 18 22 11 12 21 14 13 17 21 18 18 15 19 20 539 335 357 469 355 334 418 252 311 429 450 485 594 636 372 393 615 502 433 566 374 368 502 545 626 719 582.3 352.1 372.4 558.5 445.3 386.3 498.7 314.2 337.8 465.4 502.6 556.2 664.4 524 334 366 432 355 283 396 233 293 429 411 449 515 551 355 393 550 461 439 533 305 352 481 470 583 622 535.3 339.1 374.8 510.9 403.3 356.9 447.7 263.2 328.4 448.3 440.1 497.7 576.6 521 334 357 432 350 283 396 229 284 421 411 377 504 524 334 366 498 417 352 456 237 309 437 411 463 601 522.3 334.0 361.1 467.3 388.8 332.8 435.8 232.2 294.1 432.0 411.0 436.8 569.8 826 Int J Adv Manuf Technol (2012) 59:815–828 Table Computational results of problem in experiment ICA TSa SAa GAa Table Computational results of problem in experiment PSOb Codition (a) ICA SAa Condition (a) Mean 2527.5 2609.6 2668.5 2796.0 2680.5 Maximum 2530.0 2690.0 2829.0 2885.0 – Minimum 2525.0 2527.0 2535.0 2667.0 2535.0 Mean 2090.0 2208.0 2287.0 2370.0 – Codition (b) Minimum 743.0 833.0 Minimum 1,198.0 1,288.0 Condition (b) a Results are taken from [9] Maximum 2090.0 2390.0 2380.0 2580.0 – Minimum 2090.0 2120.0 2120.0 2220.0 – a Results are taken from [10] b Results are taken from [12] different objectives and different manufacturing conditions in actual shop floor on the results of process planning were studied The performance of ICA was compared with some existing algorithms including GA, SA, TS, and PSO developed for the process planning problem 5.4.1 Problem Problem is taken from Li et al [1] It has 20 operations, and the following two conditions are considered (a) All machines and tools are available, and w1–w5 in Eq 11 are set as 1; (b) All machines and tools are available, and w2 =w5 =0, w1 = w3 =w4 =1; It can be seen from Table that ICA obtained new better solutions under condition (a) and condition (b) The robustness of ICA under the two conditions is better than that of GA, SA, TS, and PSO 5.4.2 Problem The second part used by Li et al [10] consists of 14 operations Two conditions are considered for studies on this part (a) All machines and tools are available, and w1–w5 in Eq 12 are set as 1; (b) All machines and tools are available, and w2 =w5 =0, w1 = w3 =w4 =1; Table shows that the proposed ICA can provide competitive results on two conditions 5.4.3 Problem The third part presented by Ma et al [9] consists of nine features and 13 operations The following two conditions are considered for studies on this part All machines and tools are available; Machine is down; Table shows that ICA outperforms SA in both condition (a) and condition (b) And new better solutions are achieved 5.4.4 Problem The fourth part is presented by Guo et al [12] to test the efficiency of PSO on process planning problem This part consists of 11 features and 14 operations Only the condition that all machining resources are available is considered Table shows that ICA outperforms GA, SA, and PSO in this problem 5.4.5 Problem Table Computational results of problem in experiment ICA TSa SAa GAa Mean Maximum Minimum 1,328.0 1,328.0 1,328.0 1,342.0 1,378.0 1,328.0 1,373.5 1,518.0 1,328.0 1,611.0 1,778.0 1,478.0 Mean Maximum Minimum 1,170.0 1,170.0 1,170.0 1,194.0 1,290.0 1,170.0 1,217.0 1,345.0 1,170.0 1,482.0 1,650.0 1,410.0 Codition (a) This part is used by Zhang et al [7] to test GA’s capability and flexibility of handling process planning problems under different requirements It contains 19 machining features and a total number of 23 operations In this paper, the Table Computational results of problem in experiment Codition (b) a Results are taken from [10] Mean Minimum a ICA PSOa SAa GAa 1,364.1 1,357.0 1,430.0 1,361.0 1,447.4 1,421.0 1,459.4 1,381.0 Results are taken from [12] Int J Adv Manuf Technol (2012) 59:815–828 following four conditions were considered to compare the performance of ICA and GA Following the work of Zhang et al [7], 60 trials were conducted for each condition Minimizing the total weighted cost The average machining cost over 60 trials is 1,741; the minimum machine cost is 1,739, and the maximum machining cost is 1,749 The frequency of the machining cost (1,739) is much higher than that of other cost Minimizing the number of machine changes only All the 60 trials find a process plan with zero machine changes Minimizing the number of setup changes only All the 60 trials find a process plan with zero setup changes Minimizing the number of tool changes only All the 60 trials find a process plan with zero tool changes Discussions It can be seen from the computational results of the above three experiments that ICA has advantages over existing approaches in solving the process planning problems with various flexibilities In both experiment and experiment 2, ICA shows better efficiency and robustness compared with GA and SA In experiment 3, the performance of ICA was compared with that of GA, SA, TS, and PSO; experiments on different problems indicate that ICA is superior to these existing algorithms in computational effectiveness and efficiency Conclusions This paper discusses the process planning problem in which various flexibilities are considered Optimization of process planning is a NP-hard problem and efficient heuristic algorithms should be proposed to obtain near-optimal solutions with reasonable computational cost In this paper, we first concentrated the process planning problem with various flexibilities Then, a novel metaheuristic algorithm, that is, ICA was utilized to find near-optimal solutions The performance of the proposed ICA was validated over three experiments using benchmark problems taken from literature and also compared with many other algorithms developed for the optimization of process planning in the literature Computational results show the efficiency of the algorithm In future, the proposed ICA could be employed to solve some other industrial optimization problems In addition, process planning problem with various flexibilities considered in this paper could be applied in industrial applications 827 Acknowledgment This research is supported by the State Key Program of National Natural Science of China (Grant No 51035001), National Natural Science Foundation of China (Grant NO 50825503), and National Natural Science Foundation of China (Grant No 50875101) Reference Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts Int J Prod Res 40(8):1899–1922 doi:10.1080/00207540110119991 Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling Comput Oper Res 30(8):1151–1171 Seok Shin K, Park J-O, Keun Kim Y (2011) Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm Comput Oper Res 38(3):702–712 Liu X-j, Yi H, Ni Z-h (2010) Application of ant colony optimization algorithm in process planning optimization Journal of Intelligent Manufacturing (in press) Leo A, Hongchao Z (1989) Computer-aided process planning: the state-of-the-art survey Int J Prod Res 27(4):553 Marri HB, Gunasekaran A, Grieve RJ (1998) Computer-aided process planning: a state of art Int J Adv Manuf Technol 14 (4):261–268 doi:10.1007/bf01199881 Zhang F, Zhang YF, Nee AYC (1997) Using genetic algorithms in process planning for job shop machining Evol Comput IEEE Transac 1(4):278–289 Qiao L, Wang X-Y, Wang S-C (2000) A GA-based approach to machining operation sequencing for prismatic parts Int J Prod Res 38(14):3283–3303 Ma GH, Zhang YF, Nee AYC (2000) A simulated annealing-based optimization algorithm for process planning Int J Prod Res 38 (12):2671–2687 10 Li WD, Ong SK, Nee AYC (2004) Optimization of process plans using a constraint-based tabu search approach Int J Prod Res 42 (10):1955–1985 doi:10.1080/00207540310001652897 11 Li L, Fuh JYH, Zhang YF, Nee AYC (2005) Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments Robot Comput-Integr Manuf 21 (6):568–578 12 Guo Y, Mileham A, Owen G, Li W (2006) Operation sequencing optimization using a particle swarm optimization approach Proc Inst Mech Eng, Part B: J Eng Manuf 220(12):1945–1958 13 Salehi M, Tavakkoli-Moghaddam R (2009) Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning Eng Appl Artif Intell 22(8):1179–1187 14 Shao X, Li X, Gao L, Zhang C (2009) Integration of process planning and scheduling—a modified genetic algorithm-based approach Comput Oper Res 36(6):2082–2096 15 Leung CW, Wong TN, Mak KL, Fung RYK (2010) Integrated process planning and scheduling by an agent-based ant colony optimization Comput Ind Eng 59(1):166–180 16 Li X, Gao L, Shao X, Zhang C, Wang C (2010) Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling Comput Oper Res 37(4):656–667 17 Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition In: Evolutionary Computation CEC 2007 IEEE Congress on, p 2007 pp 4661–4667 18 Atashpaz-Gargari E, Caro L (2007) Designing an optimal PID controller using Colonial Competitive Algorithm In: First Iranian Joint Congress on Intelligent and Fuzzy Systems 828 19 Atashpaz-Gargari E, Hashemzadeh F, Lucas C (2008) Designing MIMO PIID controller using colonial competitive algorithm: applied to distillation column process In: Evolutionary Computation, 2008 CEC 2008 (IEEE World Congress on Computational Intelligence) IEEE Congress on pp 1929–1934 20 Gargari EA, Hashemzadeh F, Rajabioun R, Lucas C (2008) Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process Int J Intell Comput Cybern (3):337–355 doi:10.1108/17563780810893446 21 Rajabioun R, Atashpaz-Gargari E, Lucas C (2008) Colonial competitive algorithm as a tool for Nash equilibrium point achievement In: Gervasi O, Murgante B, Laganà A, Taniar D, Mun Y, Gavrilova M (eds) Computational science and its applications—ICCSA 2008, vol 5073 Lecture Notes in Computer Science Springer Berlin, Heidelberg, pp 680–695 doi:10.1007/ 978-3-540-69848-7_55 22 Khabbazi A, Gargari EA, Lucas C (2009) Imperialist competitive algorithm for minimum bit error rate beamforming Int J BioInspir Comput 1(1/2):125–133 doi:10.1504/IJBIC.2009.022781 23 Forouharfard S, Zandieh M (2010) An imperialist competitive algorithm to schedule of receiving and shipping trucks in crossdocking systems Int J Adv Manuf Technol 51(9):1179–1193 doi:10.1007/s00170-010-2676-5 24 Kaveh A, Talatahari S (2010) Optimum design of skeletal structures using imperialist competitive algorithm Comput Struct 88(21–22):1220–1229 25 Lucas C, Nasiri-Gheidari Z, Tootoonchian F (2010) Application of an imperialist competitive algorithm to the design of a linear induction motor Energy Convers Manag 51(7):1407–1411 26 Nazari-Shirkouhi S, Eivazy H, Ghodsi R, Rezaie K, AtashpazGargari E (2010) Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm Expert Syst Appl 37(12):7615–7626 27 Sarayloo F, Tavakkoli-Moghaddam R (2010) Imperialistic competitive algorithm for solving a dynamic cell formation problem with production planning In: Huang D-S, Zhao Z, Bevilacqua V, Figueroa J (eds) Advanced Intelligent Computing Theories and Applications, vol 6215 Lecture Notes in Computer Science Springer Berlin, Heidelberg, pp 266–276 doi:10.1007/978-3-64214922-1_34 28 Sayadnavard MH, Haghighat AT, Abdechiri M Wireless sensor network localization using imperialist competitive algorithm In: Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, 2010 pp 818–822 Int J Adv Manuf Technol (2012) 59:815–828 29 Shokrollahpour E, Zandieh M, Dorri B (2010) A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem Int J Prod Res 49(11):3087–3103 30 Moghimi Hadji M, Vahidi B (2011) A solution to the unit commitment problem using imperialistic competition algorithm Power Syst, IEEE Trans on PP 99:1–1 31 Bagher M, Zandieh M, Farsijani H (2010) Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm The International Journal of Advanced Manufacturing Technology: 1– 15 doi:10.1007/s00170-010-2937-3 32 Niknam T, Taherian Fard E, Pourjafarian N, Rousta A (2011) An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering Eng Appl Artif Intell 24(2):306–317 33 Abdechiri M, Faez K, Bahrami H (2010a) Adaptive imperialist competitive algorithm (AICA) In: Cognitive Informatics (ICCI) 9th IEEE International Conference on, p 2010 pp 940–945 34 Abdechiri M, Faez K, Bahrami H (2010b) Neural network learning based on chaotic imperialist competitive algorithm In: Intelligent Systems and Applications (ISA) 2nd International Workshop on, p 2010 pp 1–5 35 Bahrami H, Faez K, Abdechiri M (2010) Imperialist competitive algorithm using chaos theory for optimization (CICA) In: Computer Modelling and Simulation (UKSim) 12th International Conference on, p 2010 pp 98–103 36 Duan H, Xu C, Liu S, Shao S (2010) Template matching using chaotic imperialist competitive algorithm Pattern Recognit Lett 31(13):1868–1875 37 Karimi N, Zandieh M, Najafi AA (2010) Group scheduling in flexible flow shops: a hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism International Journal of Production Research (in press) 38 Ho YC, Moodie CL (1996) Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities Int J Prod Res 34(10):2901–2923 39 Tseng HE (2006) Guided genetic algorithms for solving a larger constraint assembly problem Int J Prod Res 44(3):601–625 doi:10.1080/00207540500270513 40 Kim YK (2003) A set of data for the integration of process planning and job shop scheduling http://syslab.chonnam.ac.kr/ links/data-pp&s.doc 41 Test-bed problems for multi-objective FMS process planning using multi-objective symbiotic evolutionary algorithm (2010) http://syslab.chonnam.ac.kr/links/MO_FMS_PP_MOSEA.doc Int J Adv Manuf Technol (2012) 59:829–839 DOI 10.1007/s00170-011-3531-z ORIGINAL ARTICLE Disassembly sequence planning approach for product virtual maintenance based on improved max–min ant system Xinhua Liu & Gaoliang Peng & Xiumei Liu & Youfu Hou Received: 24 June 2010 / Accepted: 12 July 2011 / Published online: August 2011 # Springer-Verlag London Limited 2011 Abstract In order to realize automation and intelligence of product disassembly process in a virtual maintenance environment, an improved max–min ant system based methodology for product disassembly sequence planning was proposed The feasibility graph for product disassembly process was defined and the mathematic model of product disassembly sequence planning problem was set up Thus, the problem of product disassembly sequence planning was transformed into the problem of searching optimal path on a feasibility graph Moreover, an improved max–min ant system based on the strategy of sorting elite ants was presented and the flowchart of the improved algorithm was designed Finally, by simulation examples, the robustness and outperforming others of the improved algorithm were verified Keywords Product disassembly sequence planning Virtual maintenance Feasibility graph Max–min ant system Introduction Nowadays, the growing amount of waste generated at the end-of-life of products has become a severe problem in many countries and the concept of green manufacturing has become more popular in the engineering area [1] As X Liu (*) : X Liu : Y Hou School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China e-mail: l_xinhua_2006@126.com G Peng School of Mechanics and Electronics, Harbin Institute of Technology, Harbin, China an important technology that ensures the products can be used with long time and reused with high efficiency, virtual maintenance has been an active research since the past decades In general terms, product maintenance process is composed of many tasks such as maintenance evaluation, maintenance planning, maintenance process simulation and maintenance operation, etc As the first and crucial task of product maintenance process, product disassembly sequence planning decides whether the product maintenance process can be performed automatically and intelligently with high efficiency and low cost The problem of product disassembly sequence planning involves the generation of one or more feasible sequences to disassemble a product successfully Traditionally, researches on the problem of product disassembly sequence planning have often made use of graph models to represent product disassembly architecture, collect, and store relevant product disassembly information, such as adjacency graph or adjacency matrix, AND/OR graph and precedence graph, etc [2–6] The main contents of these researches can be summarized as follows: (1) how to construct the feasibility graph to represent product disassembly architecture and (2) how to generate one or more feasible sequences on the feasibility graph The problem of product disassembly sequence planning is a non-deterministic polynomial-time hard (NP-hard) combinatorial optimization problem [7] Generally, with the increase of parts or components in a product, the computational complexity of searching for optimal product disassembly sequence in a large solution space will increase more quickly Therefore, traditional methods cannot solve this problem effectively—that is, avoid the combinatorial explosion Consequently, heuristic methods and artificial intelligence (AI)-based algorithms are often used to find out the optimal solution with a high efficiency 830 Bearing the above observations in mind, we apply an improved max–min ant system based algorithm to the problem of product disassembly sequence planning and the rest of this paper is organized as follows In Section 2, some related works are outlined based on literature In Section 3, the feasibility graph for product disassembly process was defined Section presents the framework of the proposed methodology and the mathematic model of product disassembly sequence planning problem was set up Section describes the improved max–min ant system based algorithm and designs the flowchart of the proposed algorithm Section provided two simulation examples to verify the improved algorithm and discuss the differences of genetic algorithm, simple max– ant system and improved max–min ant system Finally, Section concludes with some advantages and limitations of our improved algorithm, and points out some remaining future work Literature review Recent publications relevant to this paper are mainly concerned with two research streams: product disassembly sequence planning and ant colony optimization In this section, we try to summarize the relevant literature 2.1 Product disassembly sequence planning For product disassembly sequence planning, many researchers have worked on the problem and proposed different solutions since the last decades In [8], Pitipong et al presented a case-based reasoning approach for automation disassembly process planning In [9], Dong proposed a hierarchical approach to disassembly sequence planning for mechanical product In [4], the product or equipment under maintenance is modeled using a hybrid graph, with maintainable components or subassemblies as nodes, and disassembly constraints as edges In [5], Moore et al presented a Petri net-based approach to automatically generate disassembly process plans for product recycling or remanufacturing There are other approaches for disassembly sequence planning such as the scatter search approach [10], the object-oriented approach [6] and the life-cycle analysis approach [11] etc Recently, heuristic methods and AI-based algorithms were applied to disassembly sequence planning such as genetic algorithm [1, 7, 11, 12], neural network approach [13], ant colony optimization [14], particle swarm optimization [15], and greedy randomized adaptive search procedure algorithm [16], etc To deliver a robust solution applicable to practical problems, a few research- Int J Adv Manuf Technol (2012) 59:829–839 ers combined two or more factors in an integrated approach for the problem of product disassembly sequence planning [17, 18] To automatic the process of product disassembly sequence planning with high efficiency and intelligence, some researchers pay much attention to the problem of selective disassembly sequence planning [19, 20] 2.2 Ant colony optimization The natural metaphor on which ant algorithms were based was that of ant colonies Researchers were fascinated by seeing the ability of the almost blind ants in establishing the shortest route from their nests to the food source and back These ants secreted a substance, called a pheromone and used its trails as a medium for communicating information among each other The probability of the trail followed by other ants was enhanced by increased trail deposition of others followed this trail This cooperative search behavior of real ants inspired the new computational paradigm for optimizing real-life systems and it was suited for solving NP-hard combinatorial optimization problems [21, 22] Following from the original ant system (AS), various improvements such as max–min ant system (MMAS), ant colony system, rank-based ant system etc., [23–25] were made which gave rise to several other ant algorithms which collectively form the main ant colony optimization (ACO) algorithms Nowadays, the ACO algorithms have been applied to several combinatorial optimization problems and got outstanding results, such as traveling salesmen problem, job shop scheduling problem, vehicle routing problem, quadratic assignment problem, group technology applications, etc [26–29] 2.3 Discussion However, although many approaches to generate disassembly sequence have been developed in above literature, they have some common disadvantages summarized as follows Firstly, they cannot sieve illogical disassembly sequence Secondly, the number of disassembly schemes tends to be explosive in combination optimization problems, and it is difficult for them to differentiate different relations and to generate disassembly sequences Finally, few researches have focused on three-dimension disassembly in a virtual maintenance environment The research work presented in this paper, by converting the problem of product disassembly sequence planning into the problem of a search optimal path on a feasibility graph, uses improved max–min ant systembased algorithm to search for the optimal product disassembly sequence Int J Adv Manuf Technol (2012) 59:829–839 831 Feasibility graph of product disassembly sequence Framework of the proposed methodology The representation of the product disassembly sequence is a key issue in the process of product disassembly sequence planning A well-designed representation scheme for product disassembly sequence planning is able to assist maintenance engineers in performing maintenance tasks efficiently In this section, we try to elaborate a directed graph called feasibility graph for the representation of product disassembly sequence Formally, the feasibility graph for the representation of product disassembly sequence can be defined as follows: The framework of the proposed methodology for product disassembly sequence planning in a virtual maintenance environment is composed of two steps: one is feasibility graph construction and the other is product disassembly sequence planning shown in Fig Firstly, maintenance product information was input into the system and a feasibility graph was constructed to represent all feasible disassembly sequences for the maintenance product Secondly, considering all disassembly constraint information, such as adjacency and constraint relations between parts and subassemblies, disassemble time, disassemble tool, disassemble priority, quality, reliability and load of disassemble machine etc., an improved max–min ant system-based algorithm was proposed to search the optimal product disassembly sequence based on the strategy of sorting elite ants Thus, the problem of product disassembly sequence planning was transformed into the problem of searching optimal path on the feasibility graph There are three ways to construct the feasibility graph for a maintenance product such as knowledge-based reasoning approach, case-based reasoning approach, and workflow modeling technology-based approach In our paper, the problem of feasibility graph construction was not the primary research and we try to focus on solving the second step of the proposed methodology According to the definitions of feasibility graph and the framework of proposed methodology, the mathematic model for the problem of searching optimal path on a feasibility graph can be described as follows: Definition A feasibility graph for the representation of product disassembly sequence is abstracted as a three-tuple: G ¼ fV ; E; Rg Where G is the feasibility graph for the representation of product disassembly sequence; V ¼ fv1 ; v2 ; ; g is a set of nodes and a node represents a part or a subassembly There is only one start node and one end node The value of a node is named after computing cost; E ¼ fe1 ; e2 ; ; em g is a set of edges describe the flows between those nodes and ei(vi0,vi1) is a directed edge from vi0 to vi1 We called vi0 as front node and vi1 as back node of the edge ei Obviously, ei(vi0,vi1) is unequal to ei(vi1,vi0) The value of a directed edge is named after communicating cost; R ¼ fr1 ; r2 ; :::; rk g is a set of relations between the nodes and the edges, and there are three relations such as AND, OR, and complex AND/OR Definition Si is a set of nodes that are back of nodes vi and L is a feasible product disassembly sequence The feasibility graph for a maintenance product can represent the adjacency and constraint relations between parts and subassemblies Additionally, the feasibility graph also includes other product disassembly information such as disassemble time, disassemble tool, disassemble quality etc., which play an important role in computing the execution cost of disassembly tasks According to the above definitions, an example of feasibility graph with 11 nodes and 12 edges was illustrated as Fig Fig An example of feasibility graph Min n P giị Cei iẳ1 s:t: & giị ¼ 1; 0; ei selected ei unselected gð1Þ  1; gnị  viỵ1 Si i ẵ1; nị C ei ¼ W  K T K ¼ fKs ; Kd g ¼ fk1 ; k2 ; k3 ; Á Á Á ; km g W ¼ fw1 ; w2 ; w3 ; Á Á Á ; wm g m P wi ¼ wi > 0ði ¼ 1; 2; 3; Á Á Á mÞ where, Cei is the execution cost of the node vi composed of computing cost and communication cost, K is a set of 832 Int J Adv Manuf Technol (2012) 59:829–839 Fig Framework of the proposed methodology execution cost factors composed of static factors Ks and dynamic factors Kd, W is a set of weighting coefficients for execution cost factors, n is the number of nodes on a feasibility graph, and m is the number of execution cost factor In order to guarantee dimensions uniform, some transformations were applied to normalize the parameters and the formula was described as follows: m X ki j ẵ1; m kj ẳ kj = iẳ1 Improve max–min ant system-based algorithm The MMAS algorithm differs from the original AS in two main ways: one is that only the best ant updates the pheromone trials and the other is that the pheromone update function is bound In order to formulate the problem in mathematical expression, the following notations are introduced first: pkij ðtÞ Probability of ant k taking an edge e(vi,vj) at time t Int J Adv Manuf Technol (2012) 59:829–839 Cij (t) ηij (t) α β T δ ρ1 C0 Cmin Cmax ρ2 Δ Cij (t) Q Lbest σ ω C m N t k i Pheromone associated to the edge e(vi,vj) at time t Visibility value of the edge e(vi,vj) at time t Weight of existing pheromone (trail) in path selection Weight of a given edge in path selection Constant Adjustment variable Variable that controls the local pheromone evaporation rate and 0 vs 2Sik > > : 0; otherwise And the parameter ηij (t) can be defined as follows: À Á hij tị ẳ T = d ỵ Cej 5.2 Pheromone trail updating In order to improve the quality of future solutions, the pheromone trails of ants must be updated to reflect the performance of ants and the quality of solutions obtained This updating is a key element to the adaptive learning technique of max–min ant system and helps to ensure the t rij tị ẳ 1=Lr tị s X w rịt rij tị ỵ wt best ij tị r¼1 In order to avoid stagnation of the searching optimal solution, the range of possible pheromone trails on each solution component is limited to a bound [Cmin, Cmax], and the pheromone trails that overstep the bound should be configured as follows: & t ij tị ẳ t max ; t ij tị ẳ t ; if t ij tị > t max if t ij ðtÞ < t Obviously, the values of Cmax and Cmin play an important role in searching optimal solution and they are associated to the optimal solution Thus, once a new optimal solution appears the values of Cmax and Cmin should be modified accordingly and the updated rule can be defined as follows: > > at beginning < ; C t max tị ẳ 1 s > > best ỵ best ; otherwise : 2ð1 À r2 Þ L L t tị ẳ t max tị 20 834 Int J Adv Manuf Technol (2012) 59:829–839 5.3 Flowchart of the improved algorithm Simulation examples According to the above description about the improved max–min ant system-based algorithm, the proposed algorithm is an iterative algorithm and can be coded easily on the computer and the pseudocode of the algorithm flowchart can be summarized as follows: In this section, two simulation examples are put forward to validate the proposed algorithm based on improved max–min ant system The first simulation example is used to verify the feasibility and efficiency of the proposed algorithm and the second simulation example is provided to prove the outperforming others of the proposed algorithm In our experiments, the configurations of simulation environment are shown in Table 6.1 Simulation example In this research, the feasibility graph for the representation of product disassembly sequence planning was set up using our self developed workflow modeling tool called WFlow The maintenance product models tested in simulation example and the feasibility graph with 26 nodes and 40 edges are illustrated as Fig The values of parameters greatly influence the quality of solution and searching speed, the influence of parameters α, β, ρ1, ρ2, σ on the performance of the proposed algorithm was shown in Fig It was observed that the optimal solution can be achieved with proper values of α, β, ρ1, ρ2, and the change of σ from eight to 35 did not influence the solution quality significantly Therefore, in this simulation example, the parameters of the proposed algorithm were configured as follows: a ¼ 0:3; b ¼ 4; T ¼ 1; d ¼ 0:1; r1 ¼ r2 ¼ 0:8; Q ¼ 26; m ¼ 26; s ¼ 8; w ¼ 26; C ¼ 50; N ¼ 200: K ¼ fk1 ; k2 ; k3 ; k4 ; k5 ; k6 g W ¼ f0:30; 0:20; 0:15; 0:15; 0:10; 0:10g where, k1 is representation of disassemble cost, k2 is representation of disassemble time, k3 is representation of disassemble tool cost; k4 is representation of disassemble priority, k5 is representation of disassemble quality, and k6 is representation of load of disassemble machine Table Configurations of simulation environment Index Parameter name Parameter content Simulation platform Operation system CPU Memory Development environment Simulation engine Data storage format PC Window Xp Sp2 2.4 GHz 2G VC++ 6.0 Matlab7.1 Excel Int J Adv Manuf Technol (2012) 59:829–839 835 Fig Simulation example The values of execution cost and its factors are shown in Table and the simulation result that is the optimal product disassembly sequence is illustrated as Fig 6.2 Simulation example In order to differ from the basic MMAS and our proposed algorithm, we called the basic MMAS as simple max–min ant system (SMMAS) and our proposed algorithm was named after improved max–min ant system (IMMAS) In this simulation example, genetic algorithm (GA), SMMAS, and IMMAS were provided to solve the problem of simulation example The configurations of simulation environment for three algorithms were uniform In GA, the number of chromosomes in the population was 26, the crossover probability is 0.95, the mutation probability is IMMAS IMMAS 10 10 Average Execution Cost Average Execution Cost 12 8 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Weight of existing pheromone in path selection Weight of a given edge in path selection Parameter α Parameter β 10 IMMAS IMMAS 60 10 50 40 Time Average Execution Cost 30 20 10 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Pheromone evaporation rate 1.8 2.0 Parameter ρ1 ρ Fig Influence of parameters on the performance of the proposed algorithm Number of Ants Parameter σ 15 25 35 836 Int J Adv Manuf Technol (2012) 59:829–839 Table Simulation parameters E k1 k2 k3 k4 k5 k6 Ce e1 2.5/0.0206 2.8/0.0231 2.4/0.0198 2.7/0.0223 2.6/0.0215 3.8/0.0314 3.1/0.0256 3.1/0.0256 5.2/0.0214 4.5/0.0185 5.3/0.0218 6.2/0.0255 6.1/0.0251 7.1/0.0292 6.3/0.0259 6.3/0.0259 10/0.0192 12/0.0230 12/0.0230 12/0.0230 15/0.0288 21/0.0403 12/0.0230 19/0.0365 2/0.0182 3/0.0273 3/0.0273 3/0.0273 2/0.0182 1/0.0091 2/0.0182 2/0.0182 2/0.0200 2/0.0200 5/0.0500 4/0.0400 1/0.0100 2/0.0200 1/0.0100 1/0.0100 30/0.0249 25/0.0207 45/0.0373 34/0.0282 33/0.0273 28/0.0232 20/0.0166 20/0.0166 0.0206 0.0222 0.0266 0.0262 0.0223 0.0270 0.0217 0.0237 3.3/0.0272 3.5/0.0289 3.7/0.0305 3.7/0.0305 3.8/0.0314 2.0/0.0165 2.0/0.0165 1.8/0.0149 2.6/0.0215 2.6/0.0215 2.8/0.0231 2.8/0.0231 4.9/0.0404 2.6/0.0215 4.4/0.0363 4.4/0.0363 4.1/0.0338 4.1/0.0338 2.3/0.0190 6.5/0.0267 7.4/0.0304 6.9/0.0284 6.9/0.0284 7.1/0.0292 3.9/0.0160 3.9/0.0160 4.1/0.0169 4.9/0.0201 4.9/0.0201 6.1/0.0251 6.1/0.0251 9.2/0.0378 4.9/0.0201 8.2/0.0337 8.2/0.0337 8.7/0.0358 8.7/0.0358 5.1/0.0210 17/0.0326 13/0.0250 14/0.0269 11/0.0211 17/0.0326 12/0.0230 10/0.0192 12/0.0230 14/0.0269 12/0.0230 11/0.0211 10/0.0192 16/0.0307 12/0.0230 20/0.0384 11/0.0211 15/0.0288 10/0.0192 15/0.0288 5/0.0455 2/0.0182 3/0.0273 3/0.0273 1/0.0091 3/0.0273 3/0.0273 3/0.0273 2/0.0182 2/0.0182 5/0.0455 5/0.0455 5/0.0455 2/0.0182 2/0.0182 2/0.0182 4/0.0364 4/0.0364 2/0.0182 2/0.0200 3/0.0300 2/0.0200 2/0.0200 2/0.0200 2/0.0200 2/0.0200 3/0.0300 3/0.0300 3/0.0300 3/0.0300 3/0.0300 3/0.0300 3/0.0300 2/0.0200 2/0.0200 2/0.0200 2/0.0200 4/0.0400 34/0.0282 34/0.0282 38/0.0315 38/0.0315 28/0.0232 32/0.0265 32/0.0265 40/0.0331 26/0.0215 26/0.0215 33/0.0273 33/0.0273 36/0.0298 26/0.0215 23/0.0191 23/0.0191 36/0.0298 36/0.0298 38/0.0315 0.0300 0.0271 0.0281 0.0272 0.0258 0.0203 0.0198 0.0217 0.0224 0.0218 0.0277 0.0274 0.0371 0.0218 0.0300 0.0274 0.0321 0.0306 0.0241 2.9/0.0239 2.7/0.0223 2.4/0.0198 2.4/0.0198 2.4/0.0198 4.1/0.0338 3.9/0.0322 3.9/0.0322 4.1/0.0338 4.1/0.0338 3.9/0.0322 0 5.7/0.0234 5.1/0.0210 4.5/0.0185 4.5/0.0185 5.5/0.0226 8.3/0.0341 8.1/0.0333 8.1/0.0333 8.3/0.0341 8.3/0.0341 8.1/0.0333 0 11/0.0211 14/0.0269 16/0.0307 13/0.0307 19/0.0365 18/0.0345 11/0.0211 15/0.0288 14/0.0269 13/0.0250 12/0.0230 0 5/0.0455 3/0.0273 3/0.0273 3/0.0273 5/0.0455 2/0.0182 3/0.0273 3/0.0273 2/0.0182 2/0.0182 3/0.0273 0 4/0.0400 2/0.0200 4/0.0400 4/0.0400 5/0.0500 3/0.0300 2/0.0200 2/0.0200 3/0.0300 3/0.0300 2/0.0200 0 28/0.0232 20/0.0166 34/0.0282 34/0.0282 40/0.0331 38/0.0315 30/0.0249 30/0.0249 38/0.0315 38/0.0315 30/0.0249 0 0.0282 0.0227 0.0252 0.0243 0.0311 0.0310 0.0281 0.0292 0.0299 0.0296 0.0284 0 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12 e13 e14 e15 e16 e17 e18 e19 e20 e21 e22 e23 e24 e25 e26 e27 e28 e29 e30 e31 e32 e33 e34 e35 e36 e37 e38 e39 e40 0.05, the penalty weight is 0.1, and the number of cycles is 200 The parameters of SMMAS and IMMAS were in common with simulation example In order to avoid the random error, each algorithm was run 200 times and calculated the average values The average deviation, max deviation, percent of best solution, and computing time of three algorithms were shown in Table and the convergence trend of three algorithms was shown in Fig It was observed that the performance of IMMAS was better than GA and SMMAS The IMMAS algorithm performed with lower average deviation, max deviation, Int J Adv Manuf Technol (2012) 59:829–839 837 Fig Simulation result higher solution quality and searching speed, and the robustness and outperforming others of the improved algorithm were verified Using the strategy of sorting elite ants, IMMAS algorithm can achieve the optimal solution with higher quality and searching speed than SMMAS algorithm As a parallel computing approach, the searching speed of IMMAS algorithm can be improved with the increase of the number of ants 6.3 Application of the proposed algorithm The proposed algorithm has been applied and validated in our self-developed virtual reality (VR)-based system for virtual maintenance shown in Fig In our self-developed VR-based system called VRproduct disassembly sequence planning, an optimal product disassembly sequence is generated through the proposed algorithm and it can be used to assist maintenance engineers in performing maintenance tasks efficiently The product disassembly sequence is listed on the right of the interface and the product maintenance process can be performed automatically and intelligently with high efficiency and low cost Table Comparison of GA, SMMAS, IMMAS Algorithms GA SMMAS IMMAS Conclusions and future work Since product disassembly sequence planning is an NPhard combinatorial optimization problem, an efficient heuristic approach is required to solve such problem In our paper, we apply an improved max–min ant systembased algorithm to solve this problem Even though the quality of product disassembly sequence generated and the converge rate are not considered optimal, the proposed algorithm is shown to be able to generate a near-optimal solution for the problem of product disassembly sequence planning in a virtual maintenance environment with a reasonable amount of time There are obvious advantages to the proposed algorithm when the sequence to be generated will involve a large number of disassembly operations (for example, complex products with a large number of components to be disassembled) or where consideration must be given for computation time The main disadvantages of the proposed algorithm are due to its sensibility on the selection of the parameters and initial conditions In future studies, the authors plan to investigate various improvements for the proposed algorithm Possible improvements may include the use of other pheromone trail updating rules and the combination of Average deviation (%) Max deviation (%) Percent of best solution (%) Computing time (min) 3.15 2.45 0.80 42.6 33.4 18.6 72.5 77.4 91.4 12.42 12.44 10.38 838 Int J Adv Manuf Technol (2012) 59:829–839 Fig Convergence trend of three algorithms GA SMMAS IMMAS Execution Cost 120 100 80 60 40 20 10 20 30 40 50 60 Number of Cycles Fig Application of the proposed algorithm 90 120 150 200 Int J Adv Manuf Technol (2012) 59:829–839 max–min ant system with other intelligent algorithms for better performance The applications of the proposed algorithm to other NP-hard combinatorial optimization problems are also an important research for the authors Acknowledgments The support of National Natural Science Foundation of China (no 51005231, 50905047 and 50975275), National Science Foundation for Post-doctoral Scientists of China (no 20100471408) in carrying out this research is gratefully acknowledged References Kongar E, Gupta SM (2006) Disassembly sequencing using genetic algorithm Int J Adv Manuf Technol 30:497–506 Li JR, Khoo LP, Tor SB (2006) Generation of possible multiple components disassembly sequence for maintenance using a disassembly constraint graph Int J Prod Eco 102:51–65 Toores F, Puente ST, Aracil R (2003) Disassembly planning based on precedence relations among assemblies Int J Adv Manuf Technol 21:317–327 Li JR, Khoo LP, Tor SB (2002) A novel representation scheme for disassembly sequence planning Int J Adv Manuf Technol 20:621–630 Moore KE, Güngör A, Gupta SM (2001) Petri net approach to disassembly process planning for products with complex AND/ OR precedence relationships Eur J Oper Res 135:428–449 Li JR, Khoo LP, Tor SB (2005) An object-oriented intelligent disassembly sequence planner for maintenance Comput Ind 56:699–718 Wang H, Xiang D, Duan GH (2008) A genetic algorithm for product disassembly sequence planning Neurocomputing 71:2720–2726 Veerakamolmal P, Gupta SM (2002) A case-based reasoning approach for automating disassembly process planning J Intell Manuf 13:47–60 Dong TY, Zhang L, Tong RF, Dong JX (2006) A hierarchical approach to disassembly sequence planning for mechanical product Int J Adv Manuf Technol 30:507–520 10 González B, Adenso-Díaz B (2006) A scatter search approach to the optimum disassembly sequence problem Comput Oper Res 33:1776–1793 11 Giudice F, Fargione G (2007) Disassembly planning of mechanical systems for service and recovery: a genetic algorithms based approach J Intell Manuf 18:313–329 12 Seo K-K, Park J-H, Jang D-S (2001) Optimal disassembly sequence using genetic algorithms considering economic and environmental aspects Int J Adv Manuf Technol 18:371–380 839 13 Huang H-H, Wang MH, Johnson MR (2000) Disassembly sequence generation using a neural network approach J Manuf Syst 19(2):73–82 14 McGovern SM, Gupta SM (2006) Ant colony optimization for disassembly sequencing with multiple objectives Int J Adv Manuf Technol 30:481–496 15 Tseng YJ, Yu FY, Huang FY (2011) A green assembly sequence planning model with a closed-loop assembly and disassembly sequence planning using a particle swarm optimization method Int J Adv Manuf Technol doi:10.1007/s00170-001-3339-x 16 Andrés C, Lozano S, Adenso-Díaz B (2007) Disassembly sequence planning in a disassembly cell context Robot Comp Int Manuf 23:690–695 17 Chung C, Peng QJ (2006) A hybrid approach to selective-disassembly sequence planning for de-manufacturing and its implementation on the Internet Int J Adv Manuf Technol 30:521–529 18 Hu D, Hu Y, Li C (2002) Mechanical product disassembly sequence and path planning based on knowledge and geometric reasoning Int J Adv Manuf Technol 19:688–696 19 Yi JJ, Yu B, Du L, Hu DQ (2008) Research on the selectable disassembly strategy of mechanical parts based on the generalized CAD model Int J Adv Manuf Technol 37:599–604 20 Aguinaga I, Borro D, Matey L (2008) Parallel RRT-based path planning for selective disassembly planning Int J Adv Manuf Technol 36:1221–1233 21 Mullen RJ, Monekosso D, Barman S, Remagino P (2009) A review of ant algorithms Exp Sys App 36:9608–9617 22 Aghaie A, Mokhtari H (2009) Ant colony optimization algorithm for stochastic project crashing problem in PERT networks using MC simulation Int J Adv Manuf Technol 45:1051–1067 23 Yin PY, Wang JY (2006) Ant colony optimization for the nonlinear resource allocation problem Appl Math Comput 174:1438–1453 24 Gajpal Y, Rajendran C, Ziegler H (2006) An ant colony algorithm for scheduling in flowshops with sequencedependent setup times of jobs Int J Adv Manuf Technol 30:416–424 25 Bell JE, McMullen PR (2004) Ant colony optimization techniques for the vehicle routing problem Adv Eng Info 18:41–48 26 Fox B, Xiang W, Lee HP (2007) Industrial applications of the ant colony optimization algorithm Int J Adv Manuf Technol 31:805– 814 27 Zecchin AC, Simpson AR, Maier HR, Leonard M, Roberts AJ, Berrisford MJ (2006) Application of two ant colony optimization algorithms to water distribution system optimization Math Comp Mod 44:451–468 28 Krishna AG, Rao KM (2006) Optimization of operations sequence in CAPP using an ant colony algorithm Int J Adv Manuf Technol 29:159–164 29 Agrawal AK, Bhardwaj P, Srivastava V (2011) Ant colony optimization for group technology applications Int J Adv Manuf Technol 55:783–795