A Novel Modular Disassembly Design Method for CNC Machine Tools Based on PSOA and TOPSIS
A Novel Modular Disassembly Design Method for CNC Machine Tools based on PSOA and TOPSIS
Yi-Xiong Feng zhejiang university Xuan-Yu Wu zhejiang university Xing Zhai zhejiang university Hao Zheng ( [email protected] ) hangzhou innovation institute, beihang university Shan-He Lou zhejiang university Jian-Rong Tan Zhejiang University
Original Article
Keywords: Modular disassembly design, Disassembly module modeling, Disassembly sequence planning, Disassembly scheme evaluation, Algorithm
Posted Date: October 26th, 2020
DOI: https://doi.org/10.21203/rs.3.rs-95130/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License ·1·
Title page
A Novel Modular Disassembly Design Method for CNC Machine Tools based on PSOA and TOPSIS
Yi-Xiong Feng, born in 1975, is currently a professor at Zhejiang University, China. He received his PhD degree from Zhejiang University, China, in 2004. His research interests include mechanical product design theory, intelligent automation and advance manufacture technology. Tel: +86-0571-87951273; E-mail: [email protected]
Xuan-Yu Wu, born in 1998, is currently a PhD candidate at State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, China. He received his bachelor degree from Harbin Engineering University, China, in 2019. His research interests include product innovation design and intelligent manufacturing. E-mail: [email protected]
Xing Zhai, born in 1991, is currently a master candidate at State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, China. He received his bachelor degree from Changan University, China, in 2013. His research interests include digitalized designing and manufactory. E-mail: [email protected]
Hao Zheng, born in 1988, is currently a research assistant at Hangzhou Innovation Institute, Beihang University, China. He received his PhD degree from Zhejiang University, China, in 2017. His research interests include data-driven design, decision-making and optimization, multi-objective evolutionary algorithms and machine learning. E-mail: [email protected]
Shan-He Lou, born in 1993, is currently a PhD candidate at State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, China. He received his bachelor degree from Wuhan University of Technology, China, in 2015. His research interests include product innovation design and intelligent manufacturing. E-mail: [email protected]
Jian-Rong Tan, born in 1954, is currently a specially-appointed professor at Zhejiang University, China and a member of the Chinese Academy of Engineering. He received his PhD degree from Zhejiang University, China, in 1987. His research interests include mechanical designing and theory, digitalized designing and manufactory. E-mail: [email protected]
Corresponding author：Hao Zheng E-mail：[email protected]
·2· Yi-Xiong Feng et al.
ORIGINAL ARTICLE
A Novel Modular Disassembly Design Method for CNC Machine Tools Based on PSOA and TOPSIS
Yi-Xiong Feng1 • Xuan-Yu Wu1 • Hao Zheng2 • Shan-He Lou1 • Jian-Rong Tan1
Received June xx, 201x; revised February xx, 201x; accepted March xx, 201x © Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2017
Abstract: Disassembly is one of the crucial issues for the green 1 Introduction remanufacturing of obsolete CNC machine tools. Meanwhile, modular design method is the guarantee of disassembly rationality, Obsolete CNC machine tools have formed large-scale which can maximize economic and environmental benefits. remanufacturing resources. Reasonable dismantling can not However, modular disassembly processes of CNC machine tools only improve the utilization rate of disused CNC machine are more uncertainty in system structures and component tools, but also reduce the adverse effect on the environment conditions. On the basis of summarizing the existing research, [1-2]. Hence, modular disassembly design extends the disassembly module modeling of CNC machine tools is traditional design to the life-cycle design in the purpose of implemented. For one advantage, the rough set theory is utilized to enhancing the product detachability [3]. cluster the parts into different disassembly modules. For another, Related works on modular disassembly design are disassembly information model is constructed by the disassembly divided into three aspects, which are disassembly module multi-constrained graph. Secondly, multi-objective mathematical modeling, disassembly sequence planning and disassembly model which contains disassembly time, disassembly benefit and scheme evaluation. Disassembly module modeling consists disassembly complexity is built. A novel particle swarm of disassembly module partition and disassembly optimization algorithm (PSOA) improved by niche technology is information modeling. Disassembly module partition can applied to the disassembly sequence planning. Last but not least, effectively avoid the explosion of information combination this work also evaluates the disassembly scheme of CNC machine in the disassembly sequence planning [4]. Tseng et al. [5] tools based on the technology for order preference by similarity to added some disassembly criteria such as contact type, an ideal solution (TOPSIS). Evaluation indicator system is combination type, tool type and accessed direction to the established and the comprehensive weight of evaluation indicators liaison graph model, and a grouping genetic algorithm was is determined by the respective advantages of entropy weight utilized to cluster parts into modules. Smith et al. [6] applied method (EWM) and analytic hierarchy process (AHP). A case the atomic theory to address design modularization study is conducted to illustrate the feasibility of the proposed problems based upon the given green principles. Ji et al. [7] models and methods. The results of this work can contribute to the quantified and aggregated modularity measures by multiple selection of the optimal modular disassembly scheme of CNC attributes of part similarity. Chang et al. [8] applied the machine tools. design structure matrix built on the modular concept to Keywords: Modular disassembly design • Disassembly module group different parts together. Qiu et al. [9] proposed a modeling • Disassembly sequence planning • Disassembly scheme disassembly module partition technology for configurable evaluation • Algorithm product based on disassembly constraint relation weighted design structure matrix. The constraint relationship in
Hao Zheng [email protected] 2 Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China 1 State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
A Novel Modular Disassembly Design Method for CNC Machine Tools Based on PSOA and TOPSIS ·3· modules is strong coupling and the constraint relationship problem into a single-objective optimization problem. The between modules is weak coupling. From what have been quality of solutions depended on the cognitive level of described above, disassembly module partition was a decision-makers. Disassembly scheme evaluation is process that clusters parts with high correlation value into actually a multi-criterion optimization decision problem the same modules in terms of module partition criteria. [22]. Das et al. [23] presented a multi-factor model Nevertheless, the fuzzy relationship among the parts was including disassembly time, disassembly accessibility, seldom considered during the disassembly module partition, disassembly tool and disassembly force to evaluate the which led to the poor quality of the acquired disassembly detachable performance of products. Sun et al. [24] modules. Disassembly information modeling expresses the established a disassembly efficiency evaluation model on geometric relationship, connection relationship and the basis of parts failure rate and its disassembly time. Tian disassembly constraint relationship among the parts [10]. et al. [25] proposed a novel dual-objective optimization Moore et al. [11] obtained the disassembly precedence model which combined the energy consumption with the matrix from product CAD models and built a disassembly traditional economic criteria. Feng et al. [26] constructed petri net model for recycling and remanufacturing. Zhang et some indicators on disassembly scheme evaluation and a al. [12] defined disassembly multi-constrained graph to fuzzy integral method was applied in evaluating the describe disassembly attribute information. Vyas et al. [13] obtained disassembly schemes. Yuan et al. [27] built a extracted disassembly relevant information from product comprehensive disassembly evaluation model based on the CAD models and determined the disassembly feasibility of fruit fly algorithm, crossover efficiency and extension-gray each component which is represented as a disassembly correlation degree and evaluated the disassembly schemes precedence matrix. Yu et al. [14] analyzed the disassembly in terms of time, economy and environment. Hence, information on automobile parts and established the indicator choice, indicator quantification and weight disassembly network graph by using AND/OR graph. Thus determination were significant problems for disassembly it could be seen that disassembly information model scheme evaluation. However, previous studies often represented the disassembly precedence relationship among considered that evaluation indicators were independent the parts in the form of a graph model. However, from each other and failed to take advantage of the disassembly information model could be depicted by subjective and objective methods comprehensively in corresponding basic disassembly information list. determining weight. Resolving the disassembly sequence planning problem is In comparison with the existing studies, three distinctive under the driving of certain constraints and goals [15]. Li et contributions have been made in this paper: al. [16] utilized the disassembly constraint graph to generate (1) The rough set theory is applied to the disassembly possible disassembly operations and genetic algorithm was module partition of CNC machine tools and the disassembly employed to acquire optimal disassembly sequence. Dong information model is constructed by the disassembly multi- et al. [17] presented an automatic disassembly sequence constrained graph. generation approach from hierarchical attributed liaison (2) Multi-objective mathematical model which contains graph representation. Smith et al. [18] created a disassembly disassembly time, disassembly benefit and disassembly sequence structure graph model (DSSG) and used rules to complexity is built. The PSOA improved by niche search feasible solutions from the DSSG. Zhang et al. [19] technology is utilized to solve the mathematical model. defined some disassembly constraint factors and adopted the (3) The grey correlation theory and the Euclidean fuzzy-rough set mapping model to generate the optimum distance are imported into the TOPSIS. The comprehensive parallel disassembly sequence. Kim et al. [20] represented weight of evaluation indicators is determined by the possible disassembly sequences using an extended process respective advantages of the EWM and the AHP. graph, and a branch-bound algorithm is proposed to The rest of this paper is organized as follows. Section 2 decrease the search space. Tian et al. [21] took the uncertain illustrates the disassembly module modeling of CNC part quality and varying operational cost into consideration machine tools, and disassembly sequence planning of CNC and presented a hybrid intelligent algorithm integrating machine tools is analyzed in Section 3. Section 4 presents fuzzy simulation and artificial bee colony to solve graph- the disassembly scheme evaluation of CNC machine tools. based disassembly sequence. Although the graph and A case study is presented in Section 5. Finally, Section 6 intelligent algorithm in solving disassembly sequence were concludes our work. committed by researchers, traditional methods paid more attention to transform the disassembly sequence planning
·4· Yi-Xiong Feng et al.
2 Disassembly module modeling 2.2 Correlation analysis among the parts based on rough set theory 2.1 Analysis on module partition criteria The correlation value is generally quantified with fuzzy and From the perspective of facilitating disassembly modeling, imprecise value which can affect the construction quality of decreasing disassembly cost, reducing disassembly disassembly modules. Rough set theory can effectively complexity and protecting the environment, module analyze the uncertain information and rough number is partition criteria are summarized as functional interaction proposed based on the rough set theory which expresses its criterion, disassembly difficulty criterion, material size in the form of interval [28]. The upper limit and the similarity criterion and contact type criterion. lower limit of the interval can not only reflect the size of (1) Functional interaction criterion. The functional data, but also reflect the distribution of data. The quality of interaction criterion divides the parts with strong functional disassembly modules can be effectively improved by interaction into the same module, which can achieve the analyzing the correlation value among the parts with the purpose of strong coupling within the modules and weak rough number. coupling among the modules. (1) Construction of the rough number g (2) Disassembly difficulty criterion. The disassembly th i( j , ) expresses that expert g (1≤g≤G) marks the difficulty criterion, which is generally determined by part correlation value between part i and j on the criterion h. The connection types, clusters the parts with large disassembly correlation value between part i and j is described as Table 12 gG difficulty into the same modules. 1. The set Utttt hijhijhijhij( ,)( ,)(,,...,,... ,)( ,) consists of G (3) Material similarity criterion. The material similarity correlation values between part i and j on the criterion h. If criterion divides the parts with same or similar materials into the size of the correlation value in set U is 12 gG the same module, which is conducive to the material tttth( ijh ,)( ijh ,)( ,)(ijh ,) ij ...... , the rough number can recycling and ensures the material performance. be obtained as follows [29]. ggg (4) Contact type criterion. The different spatial position ttth( ijh ,)( ijh ,)( ,)ij[,] (1) g relationship among the parts brings about different contact gm th(,)(,) i j t h i j / g (2) types. Meanwhile, different contact types determine m1 different disassembly complexity among the parts. G g m ttGgh( i , jh )( i , j ）/ (1) (3) The definition of module partition criteria among the mg parts is described and quantified as Figure 1.
Functional interaction among the parts Table 1 The correlation value between part i and j Relationship type Relationship description Value Expert h=1 h=2 h=3 h=4 Stronger Inseparable function and structure 1 1 1 1 1 Strong Consistent function and compact structure 0.8 1 t1 ( ,ij ) t2(,)ij t3(,)ij t4(,)ij Normal General functional interaction and structural association 0.6 2 2 2 2 Weak Weak functional interaction and loose structural association 0.4 2 t1 ( ,ij ) t2(ij , ) t3(ij , ) t4(ij , ) Weaker Basically independent 0.2 None uncorrelated 0 G G G G Disassembly difficulty among the parts G t t t t ⋮ 1 ( ,ij⋮ ) 2(,)⋮ij 3(,)⋮ij 4(,)⋮ij Disassembly difficulty type Connection type description Value Harder Non-detachable connection 1 Hard fastener 0.8 (2) Calculation of the comprehensive correlation value Normal Swelling connection 0.6 Easy Clearance fit 0.4 The average correlation value Rt()hij(,) and the Easier Shape connection 0.2 None uncorrelated 0 comprehensive correlation value Rt()(,)ij between part i Materials compatibility among the parts and j is formulated as follows. Relationship type Value G Compatible extremely 1 1 g Compatible 0.8 R() tth( i , jh )( i , j ) (4) g 1 Normal 0.6 G Compatible difficultly 0.4 4 Incompatible 0.2 R()() tR(i , jhh )(t , i ) j (5) Incompatible extremely 0 h1 Contact type among the parts Contact type Value where h is the weight of module partition criterion h, Polyhedral contact 1 which is determined by the AHP. Multipoint contact 0.8 Single surface contact 0.6 The comprehensive incidence matrix R can be established Line contact 0.4 Point contact 0.2 with Eqs. (4), (5). Non-contact 0 Figure 1 Module partition criteria among the parts
A Novel Modular Disassembly Design Method for CNC Machine Tools Based on PSOA and TOPSIS ·5·
multi-constrained graph is a variant of disassembly hybrid R()()[,][,] t(1,1) R t (1,n ) L 11 U 11 L 1n U 1 n graph, which can effectively reveal the disassembly R= constraint information and disassembly precedence R()()[,][,] t(n ,1) R t ( n , n ) L n 1 U n 1 L nn U nn information. Disassembly multi-constrained graph can be (6) defined by a quintuple, which is represented as follows. where Lij is the lower limit of interval, Uij is the upper limit of interval and n is the number of the parts. =(,,,,)VEEECpspr (12) (3) Clustering solution and result optimization where VVVV , ,..., indicates all the disassembly The transitive closure method is used to transform the 12 N modules, Ep is physical constraint which illustrates that comprehensive incidence matrix into fuzzy equivalent there are direct contact and identical disassembly matrix and the result of module partition can be obtained by precedence among the disassembly modules, Esp is powerful using upper threshold value and lower threshold value U physical constraint which represents that there are direct . If L and U , part i and j belong to the L ij L ij U contact and different disassembly precedence among the same module. Otherwise, part i and j belong to the different disassembly modules, Er is spatial constraint which modules. Although clustering parts with upper and lower describes that there are disassembly precedence but without threshold values can be better than using single threshold direct contact among the disassembly modules, C is linkage value, the selection of threshold values will affect the quality constraint. Figure 2 is a paradigm of disassembly multi- and number of disassembly modules. The clustering constrained graph. principle is to ensure higher cohesion degree within the modules and lower coupling degree among the modules [30]. Thus, the cohesion degree and coupling degree can be utilized to guide the reasonable selection of the upper and lower threshold value.
The cohesion degree MI K of module K and the coupling degree NI between module α and β are defined as follows. nn1
MIRKi jikjkitb bRt j ()/()( , )max( , ) (7) ij11 i
nn
NIR tb bRt ()/()(i , jiji )max( j , ) (8) ij11 if bik =1 , part i belongs to module K; otherwise, bik =0 . Figure 2 Disassembly multi-constrained graph The total cohesion degree MI, the total coupling degree NI and the ratio γ between MI and NI are described as 3 Disassembly sequence planning follows. 1 N 3.1 Construction of the multi-objective mathematical MI= MIk (9) N k 1 model 1 NN1 (1) The objective function for disassembly time NINI= (10) NN(1) 11 The factors that affect the disassembly time mainly MI include the connection relationship among the modules, the = (11) NI change of disassembly tools and the change of disassembly where N is the number of the modules. direction. Removing connection relationships among the modules is the main disassembly process of CNC machine The larger the value of γ is, the higher the quality of the tools. a is the total average time of disassembling the disassembly modules is. Therefore, the value of γ can be i used to guide the selection of threshold values. connection type i. atn (13) iii
2.3 Disassembly information modeling based on where ti the average disassembly time of the connection disassembly multi-constrained graph type i, ni is the number of the connection type i. It is inevitable to analyze the connection relationship and Then, removing different connection relationships need constraint relationship among the modules after the different disassembly tools and the conversion time between disassembly module partition is completed. Disassembly tool i and j is variable. Disassembly tools are divided into
·6· Yi-Xiong Feng et al. five types, and the conversion time is shown as Table 2. comprehensively, the objective function for disassembly In addition, d is the time changing disassembly direction complexity is expressed as follows. ij 푖푗 from module i to j, which is formulated푡 as follows. NNNL1 Fc n ijk (,,)(,) i j k t i c tc (17) 0 unchanged i1 j i 1 i 1 c 1 dchangeij 190 (14) where and are the weights of the interface 2180change complexity and tool complexity respectively, which is determined by the AHP. n is the number of interface The objective function for disassembly time, called F , ijk t type k between module i and j. (,,)i j k is the precision of can be obtained as follows. MNNNN 11 interface type k between module i and j. tc is the positional Fadttiijij (15) accuracy of disassembly tool type c. t i( c , ) 1 , if tool type iijiiji11111 c is used when the module i is being disassembled;
otherwise, t i( c , ) 0 . Table 2 The conversion time between tool i and j s (4) Integration of objective functions Hand General Small Special Large The disassembly multi-objective mathematical model for tool tool tool tool CNC machine tools, called F(CY), is established as follows. Hand 0 2 4 6 8 min()Ft General tool 2 0 6 8 10 FCYF()min() b (18) Small tool 4 6 0 10 12 min()F Special tool 6 8 10 0 14 c
Large tool 8 10 12 14 0 where 01 , 01 and 1
(2) The objective function for disassembly benefit 3.2 The PSOA improved by the niche technology There are generally the following processing ways for the (1) Updated rules for the position and velocity of particle disassembled modules: reusing directly, reusing after The position of particle is defined as a disassembly remanufacturing, recycling and wasting directly. The first sequence. For example, the position of particle i can be three ways can bring some benefits. What’s more, the indicated as Xi x i12, x i ,..., x iN . The velocity of particle disassembly cost mainly includes the cost of tools and labors. is defined as a transformation sequence that adjusts the Thus, we have the following objective function for disassembly sequence. For instance, the velocity of particle disassembly benefit. i can be expressed as VvvviiiiN 12,,..., . If vij 0 , the N LN two modules in the corresponding position are exchanged; FzcmpEnbi ii iii() i t ici p iw (16) i1 ci11 Otherwise, vij 1. The position of the particle i after t times t where z is the benefit value of reusing directly of module iteration is X i and the velocity of the particle i after t i t times iteration is Vi . The individual and group extremum i, ci is the benefit value of reusing after remanufacturing of t t of the particle i in the t times iterations are Pi and Pg . module i, mi is the mass of module i, i is the recycling is the inertia weight factor. c1 and c2 are acceleration rate of module i, pi is the recycling price per unit of factors. r1 and r2 are random numbers between zero and module i, Ec is the cost of tool c, L is the number of tools, one. The updated formulas for the position and velocity of ni is the number of labors with regard to disassembling module i, t is the disassembly time of module i, p is particle i are presented as follows. i w tttttt1 VVciiiigi r PXc rPX1 12()() 2 (19) the labor cost per unit time. i =1 , if module i is reused t11 t t directly; otherwise, i =0 . i =1 , if module i is reused XXVi i i (20) after remanufacturing; otherwise, =0 . =1 , if module i i (2) Updating and filtrating of non-dominated solutions i is recycled; otherwise, i =0 . based on the niche technology (3) The objective function for disassembly complexity The algorithm produces a set of non-dominated solutions The disassembly complexity consists of interface after each iteration and how to deal with the non-dominated complexity and tool complexity. The interface complexity is solutions correctly is a crucial problem. This paper adopts mainly determined by the precision and number of the elite set to preserve the non-dominated solutions of each interfaces. The tool complexity is mainly determined by the iteration and utilizes the niche technology to determine the positional accuracy of tools. The precision of interfaces and fitness of particles in the elite set. The niche number of the positional accuracy of tools can be quantified as set particle i are defined as follows. V= 0.9,0.7,0.5,0.3,0.1 . Considering above factors
A Novel Modular Disassembly Design Method for CNC Machine Tools Based on PSOA and TOPSIS ·7·
K Sshdiji ((,)) (21) j1 4 Disassembly scheme evaluation dij( ,) 1( ,) dij shdij(( ,)) share (22) 4.1 Construction of evaluation indicator system share (1) Indicators about disassembly technology 0( ,) dij share Disassembly accessibility (T1) is related to connection U modes, disassembly modes and disassembly directions dijfxfx(,)(()()) titj (23) t 1 among the disassembly modules, which can be quantified where K is the size of the population, sh d( ( i , j )) is the by the fuzzy evaluation value set 1.0,0.8,0.6,0.4,0.2 . sharing function, d i( j , ) is the distance between particle i Disassembly continuity (T2) represents the consistency of and j, U is the number of optimization objective functions, disassembly direction and tool between two adjacent share is the niche radius, α is the parameter that controls the operations. Thus, the T1 and T2 of a disassembly scheme shape of the sharing function. can be calculated as When the number of non-dominated solutions in elite set N TN1 = / i (24) exceeds the maximum capacity, the non-dominated i1 solutions with low fitness are deleted. Besides, the selection TI20.50.5 (25) of optimal individual position and group position can affect 360 the efficiency of the algorithm. The optimal individual where i is the disassembly accessibility of module i, α is position can be selected by the dominant relationship. If the the changed angle of disassembly direction. If the current position of particle i dominates the historical optimal disassembly tools of two adjacent operations is identical, individual position, the historical optimal individual I 0 . Otherwise, I 1. position is updated. Otherwise, the historical optimal (2) Indicators about disassembly economy individual position is unchanged. The optimal group Disassembly cost (C1) consists of the tool cost and labor position can be selected randomly according to the fitness cost, which is formulated as of particles in elite set. L
The implementation steps of particle swarm optimization CtPF1 cci (26) i algorithm is described as Figure 3. 1 where t is the total disassembly time, P is the labor cost c per unit of time, Fci is the cost of tool i. Under the premise of obtaining the target module, the fewer the disassembled modules are, the lower the disassembly cost is. The number of disassembled modules (C2) is written as N
C2 i (27) i1
variable i takes value 1 or 0. i =1 , if the module i is
disassembled before the target module; otherwise, i =0 . (3) Indicators about disassembly environment In the process of disassembly, parts with low material compatibility should be disassembled as soon as possible. The purpose is to prevent the chemical corrosion and the reduction of material performance. Besides, the larger the proportion of discarded modules is, the higher the pressure on environment is. Therefore, the material compatibility among the parts (E1) and the proportion of discarded modules (E2) can be expressed as nn1
E1 2 ZLij, / n ( n 1) (28) i11 j i
N
EN2/ i (29) Figure 3 The implementation steps of PSOA i1
·8· Yi-Xiong Feng et al.
where ZLij, is the material compatibility value between disassembly schemes are denoted as Bbbb (,,...,)12 m part i and j, which can refer to the Table 1. Variable i and Bbbb (,,...,)12 m . If the evaluation indicator is takes value 1 or 0. i =1 , if module i is discarded; otherwise, beneficial type, the positive ideal solution B is composed
i =0 . of the maximum value of each column in matrix B and the negative ideal solution B is composed of the minimum 4.2 Evaluation of disassembly schemes based on value of each column in B. On the contrary, if the evaluation TOPSIS indicator is cost type, the positive ideal solution B is (1) Construction of the decision matrix composed of the minimum value of each column in B and the negative ideal solution B is composed of the FFFF 12,,..., n is the set of disassembly schemes and maximum value of each column in B. The Euclidean PPPP 12,,..., m is the set of evaluation indicators. Thus, the decision matrix X can be written as distance between disassembly schemes and ideal solutions can be expressed as follows. xx11 1m m 2 X (30) di b ij b j (38) j1 xxn1 nm m x 2 where ij the value of the disassembly scheme i under the dbbiijj (39) evaluation indicator j. j1 The grey correlation coefficient s and the grey (2) Determination of the comprehensive weight ij correlation value w between disassembly scheme i and This paper utilizes the EWM and the AHP to measure the i ideal solutions is described as comprehensive weight of evaluation indicators, which min minmaxbbijij max considers the weighted objectivity and subjective preference ij ij sij (40) of decision-makers. The entropy value E j and entropy max max bbijij ij weight j of evaluation indicator j can be presented as m 1 n ddijij wsi ij (41) j1 EKj ln (31) m i1 ddjj min minbbij max max ij m s ij ij (42) (1) /(1)EE (32) ij jjj max max bbij ij j1 ij
n m 1 ddjij (33) wsi ij (43) i1 m j1 nm
dDDijijij / (34) (4) Calculation of the relative closeness degree ij11 After the dimensionless processing of the Euclidean Dxxijijj / (35) distance and the grey correlation value, the relative where x j is the optimal value for column j of the decision closeness degree between disassembly scheme i and ideal matrix X, Dij is the proximity degree between xij and solutions, denoted by ci , can be presented as follows. x j , dij is the result of normalization processing on Dij . ci ci (44) The weight vector acquired from EWM is ccii 12,,..., m and the weight vector obtained from cdwiii (1) (45) AHP is ,,..., .The comprehensive weight j 12 m cdw(1) (46) of evaluation indicator j is expressed as iii m Figure 4 shows the framework of the proposed modular / (36) jjjjj disassembly design method. j1
Hence, the weighted standardized decision matrix B can 5 Case study be acquired as follows.
bb111 m The proposed methods are applied to the protective device B= (37) of CNC horizontal boring machine tool TGK46100, which bb nnm1 is displayed in Figure 5. The main part information is shown (3) Calculation of the Euclidean distance and grey in Table 3. correlation value With Eqs. (1)-(6), the comprehensive incidence matrix The positive and negative ideal solutions of the
A Novel Modular Disassembly Design Method for CNC Machine Tools Based on PSOA and TOPSIS ·9· can be constructed as Table 4. The module partition scheme According to the statistical data of production practice, is the most reasonable when the lower threshold value L the parameters of the PSOA are set as follows: 0 . 3 , and upper threshold value U are 0.64 and 0.71 cc122 , N 39 , tmax 100 , M 100 , share 50 . respectively. Therefore, the protective device can be divided Taking C12 as the disassembly target, non-dominated into the following modules, which are C1{1}, C2{2,12}, solutions about the disassembly sequence can be obtained
C3{3}, C4{4,11}, C5{5,18}, C6{6,13}, C7{7,8,9,10}, and shown in Table 6. C8{14,15,16,17}, C9{19}, C10{20}, C11{21}, C12{22},
C13{23} and C14{24}.
Figure 5 Protective device
Table 3 Detailed list of parts of protective device No. Name Material No. Name Material Right front Waterproof 1 Q235 13 24V AC plate lamp Right rear Trunking 2 preventer Q235 14 Q235 cover plate plate Right rear Trunking 3 Q235 15 Q235 plate cover plate Right rear Trunking 4 preventer Q235 16 Q235 cover plate plate Right cover 5 Q235 17 Trunking HT150 plate Droplight 6 HT250 18 Board Q235 Figure 4 The framework of the modular disassembly design plate method 7 Window Q235 19 Pull box Q235 8 Windowpane PMMA 20 Pull box plate HT150 In order to analyze the connection relationship and 9 Trim strip Q235 21 Up-spindle 45 constraint relationship among the modules, the disassembly 10 Handle HT150 22 Pull box Q235 multi-constrained graph is described in Figure 6. By Junction 11 Q235 23 Down-spindle 45 considering the relevant disassembly information, basic plate disassembly information list is constructed, which is shown Junction 12 Q235 24 Pull box plate HT150 in Table 5. plate
·10· Yi-Xiong Feng et al.
Table 4 The comprehensive incidence matrix among 24 parts Table 8 The weighted standardized decision matrix 1 2 23 24 P1 P2 P3 P4 P5 P6 0.0928 0.0831 0.0628 0.0312 0.1436 0 1 [1.0,1.0] [0.61,0.78] ⋯ [0.32,0.48] [0.12,0.17] 2 [1.0,1.0] [0.16,0.18] [0.41,0.57] 1 0.0906 0.0831 0.0615 0.0268 0.1439 0 ⋯ 퐹 [1.0,1.0] 2 0.0905 0.0831 0.0611 0.0223 0.1444 0 ⋯ 퐹 23 [1.0,1.0] [0.47,0.57] 3 0.0863 0.1245 0.0342 0.0357 0.1453 0 ⋮ ⋮ ⋮ 퐹
24 [1.0,1.0] 퐹4 Table 9 The positive and negative ideal solutions P1 P2 P3 P4 P5 P6 Positive 0.0928 0.1245 0.0342 0.0223 0.1453 0 Negative 0.0863 0.0831 0.0628 0.0357 0.1436 0
With Eqs. (38), (39), the Euclidean distance between the disassembly schemes and the ideal solutions can be calculated as follows. di (0.0510,0.0500,0.0490,0.0148) di (0.0079,0.0010,0.0142,0.0500) With Eqs. (40)-(43), the grey correlation value between disassembly schemes and the ideal solutions can be calculated as follows.
w (0.5725,0.5352,0.5117,0.3581) Figure 6 Disassembly multi-constrained graph of protective i w (0.4872,0.4262,0.5327,0.5685) device i After dimensionless processing for the Euclidean Table 5 Basic disassembly information list distance and the grey correlation value, the relative closeness degree can be obtained as Table 10. As a result, Edge Type No. Edge Type No. F C1→C2 Screw joint 6 C5→C9 Screw joint 4 we conclude that 2 is the optimal disassembly scheme.
C5→C1 Screw joint 7 C7→C2 Screw joint 10 C14→C1 Screw joint 8 C9→C10 Interference 1 Table 10 The relative closeness degree fit
C2→C3 Screw joint 7 C10→C11 Screw joint 1 → Screw joint 3 → Clearance 1 1 2 3 4 C4 C3 C11 C12 0.5263퐹 0.5710퐹 0.4170퐹 0.5536퐹 fit ∗ i C6→C5 Screw joint 4 C12→C13 Clearance 1 푐 fit Last but not least, it is necessary to compare proposed C8→C5 Screw joint 6 C14→C13 Screw joint 2 method to other well-known methods. Ant colony algorithm (ACA) and artificial bee colony algorithm (ABCA) are also Table 6 Non-dominated solutions for protective device popular swarm intelligence algorithm. Thus, they are Code Target disassembly sequence F F F t b c comparable to the improved PSOA. Besides, fuzzy 1 C8→C5→C6→C9→C10→C11→C12 264 158 6.62
2 C8→C5→C9→C10→C11→C12 251 144 6.53 comprehensive evaluation (FCE) and intuitionistic fuzzy 3 C5→C9→C10→C11→C12 232 131 6.51 evaluation (IFE) are used as benchmark methods to evaluate 4 C4→C5→C3→C2→C1→C14→C13→C12 287 219 7.23 the modified TOPSIS. The comparison results are shown in the Table 11. It can be found that the ranking results of The set of disassembly schemes is F={F , F F F } and 1 2, 3, 4 different methods are identical, which reflects the validity the set of evaluation indicators is P={P , P P P , P , P }. 1 2, 3, 4 5 6 of the proposed method. The comprehensive weight of evaluation indicators, which are determined by EWM and AHP, is shown in Table 7. The Table 11 Comparison between different methods weighted standardized decision matrix is constructed as Method Ranking result Table 8. The positive and negative ideal solutions of IPSOA+ITOPSIS F2> F4> F1> F3 disassembly schemes can be obtained as Table 9. ACA+FCE F2> F4> F1> F3 ABCA+IFE F2> F4> F1> F3
Table 7 The comprehensive weight of evaluation indicators 6 Conclusions P1 P2 P3 P4 P5 P6 weight 0.1583 0.1472 0.1748 0.1384 0.1592 0.1411
A Novel Modular Disassembly Design Method for CNC Machine Tools Based on PSOA and TOPSIS ·11·
(1) The fuzzy correlation among the parts is analyzed and the comprehensive incidence matrix is constructed in Competing interests the form of the rough number. Then, fuzzy clustering is The authors declare no competing financial interests. carried out on the parts by setting the upper and lower threshold value and clustering result is optimized by the Consent for publication cohesion degree and coupling degree. Furthermore, Not applicable Disassembly information model is built by the disassembly multi-constrained graph. Ethics approval and consent to participate (2) A disassembly multi-objective optimization model for Not applicable CNC machine tool is constructed and its purpose is to minimize disassembly time, maximize disassembly References benefit and minimize disassembly complexity. [1] G D Tian, J W Chu, H S Hu, et al. Technology innovation system Additionally, multi-objective PSOA improved by the and its integrated structure for automotive components remanufacturing industry development in China. Journal of niche technology is designed to solve the proposed Cleaner Production, 2014, 85: 419–432 model. [2] G D Tian, J W Chu, T G Qiang. Influence factor analysis and (3) An evaluation indicator system is formulated from the prediction models for component removal time in manufacturing. perspective of disassembly technology, economy and Proceedings of the institution of Mechanical Engineers, Part B: environment. The grey correlation theory and the Journal of Engineering Manufacture, 2013, 227(10): 1533–1540. Euclidean distance are imported into the TOPSIS. [3] X F Cheng, J Zhou, R B Xiao, et al. Review of modular product design from the perspective of green manufacturing. Journal of Meanwhile, the comprehensive weight of evaluation Mechanical Engineering, 2019, 6(2): 1–14. (in Chinese) indicators is determined by the respective advantages of [4] J Gao, D Xiang, G Duan. Subassembly identification based on the EWM and the AHP. The optimal disassembly grey clustering. International Journal of Production Research, scheme is selected according to the relative closeness 2008, 46(4): 1137–1161. degree between the disassembly schemes and ideal [5] H E Tseng, C C Chang, J D Li. Modular design to support green life-cycle engineering. Expert Systems with Applications, 2008, solutions. The results can be utilized to instruct 34(4): 2524–2537. decision-makers to choose target disassembly scheme [6] S Smith, C C Yen. Green product design through product when CNC machine tool is dismantled. modularization using atomic theory. Robotics and Computer Integrated Manufacturing, 2010, 26(6): 790–798. 7 Declaration [7] Y J Ji, R J Jiao, L Chen, et al. Green modular design for material efficiency: a leader-follower joint optimization model. Journal of Cleaner Production, 2013, 41: 187–201. Acknowledgements [8] T R Chang, C S Wang, C C Wang. A systematic approach for Not applicable green design in modular product development. International Journal of Advanced Manufacturing Technology, 2013, 68(9–12): Funding 2729–2741. Supported by National Key R&D Program of China [9] L M Qiu, X J Liu, S Y Zhang, et al. Disassembly modeling technology of configurable product based on disassembly (Grant No. 2018YFB1700804), National Natural Science constraint relation weighted design structure matrix. Chinese Foundation of China (Grant No. 51805472, 51775489), and Journal of Mechanical Engineering, 2014, 27(3): 511–519. Zhejiang Provincial Natural Science Foundation of China [10] L Issaoui, N Aifaoui, A Benamara. Model of mobility state of parts: (Grant No. LZ18E050001). The automation of feasibility test in disassembly sequence generation. Proceedings of the institution of Mechanical Availability of data and materials Engineers, Part C: Journal of Mechanical Engineering Science, 2017, 231(20): 3702–3714. The datasets supporting the conclusions of this article are [11] K E Moore, A Güngör, S M Gupta. Petri net approach to included within the article. disassembly process planning for products with complex AND/OR precedence relationships. European Journal of Authors’ contributions Operational Research, 2001, 135(2): 428–449. YF provided guidance for the whole research. XW and [12] X F Zhang, Z Y Hu, G Wei, et al. Disassembly modeling method for complex products based on connector structure units. Journal XZ established the model, designed the experiments and of Mechanical Engineering, 2014, 50(09): 122–130. (in Chinese) wrote the initial manuscript. HZ and SL assisted with the [13] P Vyas, J L Rickli. Automatic extraction and synthesis of results analysis. JT revised the manuscript. All authors read disassembly information from CAD assembly step file. and approved the final manuscript. Proceedings of the ASME International Design Engineering Technical Conferences, Charlotte, USA, August 21–24, 2016: 42–
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Target disassembly sequencing and scheme evaluation for CNC machine tools using Jian-Rong Tan, born in 1954, is currently a specially-appointed improved multi-objective ant colony algorithm and fuzzy integral. professor at Zhejiang University, China and a member of the IEEE Transactions on Systems, Man and Cybernetics: Systems, Chinese Academy of Engineering. He received his PhD degree 2019, 49(12): 2438–2451. from Zhejiang University, China, in 1987. His research interests [27] G Yuan, Y S Yang, G D Tian, et al. Comprehensive evaluation of include mechanical designing and theory, digitalized designing and disassembly performance based on the ultimate cross-efficiency manufactory. and extension-gray correlation degree. Journal of Cleaner E-mail: [email protected] Production, 2020, 245: 118–131. [28] Z Pawlak. Rough set approach to knowledge-based decision
Figures
Figure 1
Module partition criteria among the parts Figure 2
Disassembly multi-constrained graph Figure 3
The implementation steps of PSOA Figure 4
The framework of the modular disassembly design method Figure 5
Protective device Figure 6
Disassembly multi-constrained graph of protective device