Product assembly and disassembly sequences are the most crucial operations in product lifecycle. In this paper, an Artificial Intelligence (AI) technique, namely Genetic Algorithm (GA) is proposed to optimize product components assembly and disassembly sequences.The proposed methodology is developed and tested on an industrial product made of plastics with no integrated assembly and permanent joint parts.GA method is applied to determine the accuracy and optimum results based on 20 assembly and disassembly sequence solutions that was generated by the Design for Assembly methodology.The results indicated that GA based approach is able to obtain a near optimal solution for assembly and disassembly sequences

Daud, Ruslizam

Puteh, Nurnasran

Yasin, Azman

UUM Repository

Product Assembly and Disassembly Sequence Optimization based on Genetic Algorithm and Design for Assembly MethodologiesAzman Yasina, Nurnasran Puteha, Ruslizam DaudbaCollege of Arts and SciencesUniversiti Utara Malaysia, 06010 Sintok, KedahTel : 04-9284701, Fax : 04-9284753E-mail : yazman@uum.edu.my, nasran@uum.edu.mybSchool of MechatronicEngineeringUniversiti Malaysia Perlis, Kampus Kubang Gajah, 02600 Arau PerlisTel : 04-9798008, Fax : 04-9798227E-mail : ruslizam@unimap.edu.myABSTRACTProduct  assembly  and  disassembly  sequences  are  the  most  crucial  operations  in  product  lifecycle.  In  this  paper,  an Artificial Intelligence (AI) technique, namely Genetic Algorithm (GA) is proposed to optimize product components assembly and  disassembly sequences. The proposed methodology is developed and tested on an industrial product made of plastics with no  integrated assembly and permanent joint parts. GA method is  applied to determine the accuracy and optimum results based  on 20 assembly and disassembly sequence solutions that was  generated by the Design for Assembly methodology. The results  indicated  that  GA based  approach  is  able  to  obtain  a  near  optimal solution for assembly and disassembly sequences. KeywordsArtificial Intelligence, Genetic Algorithms, Assembly and Disassembly Sequences, Design for Assembly and Disassembly1.0 INTRODUCTIONProduct  assembly  and  disassembly  sequences  are  the  most crucial  operations  in  product  lifecycle.  Reports  showed  that cost  of  assembly  as  well  as  disassembly  of  manufactured products often contribute about 10% - 30% (sometimes higher) of  the  total  manufacturing  costs  (Kara,  Pornprasitpol  et  al. 2005). Thus,  to reduce such costs,  research works have been done  to  optimise  the  assembly  and  disassembly  sequences. Optimizations of assembly and disassembly sequences are vital as it has important significance on productivity, product quality and  manufacturing  lead  time   (Veerakamolmal,  Gupta  et  al. 1997).  This  paper  discusses  the  development  of  Genetic Algorithm  (GA)  approach  in  optimizing  the  sequence  of product  components  assembly  and  disassembly.  Although assembly is a process of constructing a mechanical object from its component parts, it is not suitable to consider disassembly as  the  reverse  of  assembly.  However,  it  can  be  relatively accepted that  some of the basic principles of assembly being adapted to disassembly. Thus, this paper will demonstrate the effectiveness  and  performance  of  the  each  proposed  method through  a  case  study  of  an  industrial  product  which  is  the Phillips Diva Iron box. In addition,  the Design for Assembly and Disassembly by the Boothroyd and Dewhurst (Boothroyd and Dewhurst,  1994) methodology  is also implemented with the proposed methods to prove that GA can be applied as a tool for assembly and disassembly sequences. 2.0 REVIEW OF PREVIOUS RELATED WORKSThere  have  been  a  number  of  research  works  reported  on methods  of  generating  assembly  sequence  as  well  as disassembly  sequence.  The  methods  include  tree  search  or graph  search  methods,  neural  network-based  approaches, simulated annealing as well as genetic algorithms. According to D.S. Hong and H.S. Cho (Hong and Cho, 1999), although the developed methods can find optimal method in assembly sequence,  it is limited to small number of part components. Meanwhile,  the  work  by  B.  Lazzerini  and  F.  Marcelloni (Lazzerini  and  Marcelloni,  2000)  generates  a  near-optimal assembly  plans  from  a  randomly  initialized  population  of assembly. It is evaluated based on three optimization criteria, namely: (i) minimizing the orientation changes of the product; (ii)  minimizing  the  gripper  replacements;  and  (iii)  grouping technologically similar assembly operations. . 3.0 SYSTEM DEVELOPMENTIn this section, the paper explains on the method of developing the system. Initially, the research problem  is to minimize the total assembly and disassembly time by proper feeder allocation and component sequencing. The standard Design for Assembly (DFA)  table  of  manual  handling  and  manual  insertion  from (Beno,  2003) were used to estimate the time of  assembly and disassembly which is measured in second. The design features that  significantly  affect  part  manual  handling  and  manual removal time are the accessibility of assemble and disassemble location,  ease  of  operation  of  assemble  and  disassembly  tool, visibility of location, ease of alignment and positioning during assembly  and  disassembly,  and  the  depth  of  removal. Meanwhile,  the manual insertion represents  the time taken for part insertion to form a unit product. On the other hand, the part features that affect manual insertion time significantly are size, thickness,  weight,  nesting,  tangling,  fragility,  flexibility, slipperiness and stickiness.  It also took into consideration  the necessity  for  using  two  hands,  necessity  for  using  grasping tools,  necessity  for  optical  magnification  and  necessity  for mechanical assistance.A graphical  representation of the above problem is shown in Table  1.  There  are  three  columns  of  nodes  representing, respectively,  the time of  insertion  (for  assembly)  or  removal (for  disassembly),  the  time  of  handling,  and  the  number  of components.  The  first  column  is  labeled  from  0 to  I(i),  the handling  time  are  from  0  to  H(i),  and  the  components  are labeled  from  1 to  i.  The  first  part  of  the  calculations  is  to calculate the time of part  removal,  the second part  calculates the  time  of  handling,  and  the  last  part  total  up  the  time  of removal and handling to get the disassembly time.Table 1: Graphical representationInsertion/ Removal timeHandling timeNumber of Components0 0 1I(i) H(i) iThe  solution  to  this  research  problem  has  been  solved separately  for  assembly  sequence  and  disassembly  sequence. Both  parts  have  applied  genetic  algorithm  approach  for optimizing assembly and disassembly sequences. Fig 1 shows the framework used for the GA Figure  1:  Framework  of  assembly  and  disassembly  sequence optimizationbased  assembly  and  disassembly  sequence  optimization respectively.  In this study, we have  compared  the GA based assembly  and  disassembly  sequences  with  the  manual assembly  sequences  using  DFA  methodology  by  Boothroyd and Dewhurst (Boothroyd and Dewhurst, 1994). In order to apply the GA method,  its inputs or chromosomes have  to be defined.  The  chromosomes  for  this  study will  be parts  of  the product  chosen,  which  is  the  Phillips  Diva  Iron box.  Each  chromosome  has  been  given  a  code  as  shown  in Table 2 below.    Table 2: Parts Name and their codesParts Name Code Item ModuleOuter Screw 1, 2 2Upper Handle 3 1Cable Tube 5 1Handle Screw 7 1Cable Screw 8, 9 2Cable Plate 10 1ALower Handle 11 1Temperature Dial 12 1Power Indicator 13 1Temperature Diode 14, 15 2Wire 16, 17, 18 3Cable Tube 6 1Upper Handle 4 1Cable 19 1BHeat Tuner 20 1Soleplate Screw 1 21 1Soleplate Screw 2 22, 23 2Soleplate Cover 24 1Rubber Hinge 25, 26 2Soleplate Assy. 27 1CGA is used as a tool in generating the optimum assembly and disassembly  sequence.  The  detail  of  GA structure  applied in this research is shown in Fig 2. GA engine is built using JAVA programming  language  and  the   KAWA  integrated development  environment.  The  GA  process  is  explained  as followed:Step 1: Generate populationsThe sequence that is generated using Design for Assembly or Disassembly manually will act as the input for the GA process. In  this  project,  there  were  30  sequences  which  are  called chromosomes in a population.Step 2: Selection (Roulette Wheel)Firstly,  the  fitness  of  each  chromosome,  fi (i  =  1  to  30)  is calculated,  which  is  the  estimated  time taken in each  of  the assembly or disassembly sequence.ii TAf =Figure 2: GA framework for assembly and disassembly sequence optimizationSecondly, the total fitness, F of the population is calculated,∑= ifFNext, the probability of each chromosome, rFi (i = 1 to 30)  is calculated,FfrFirFiii=== )301(Then the cumulative probability for each chromosome,  cFi is calculated,)302()1(1 toicFrFcFirFcFiiiii=+===−Finally, a random number, r, in the range 0 to 1, is generatedselectedisCcFrcFIf iii <<−1Step  3:  Crossover  process  (one  point  crossover)A  random number in the range (0, gene number – 1) as crossover point, cr is  generated.  Two chromosomes  at  the crossover  point  is then crossed as shown in Fig 3.Figure 3: Crossover ProcessAfter crossover process, the fitness of child 1 and child 2 were calculated.  They  will  replace  the  worst  chromosomes  in  the population where their fitness  were lower than the fitness  of these children.Step 4: Mutation processA random number in the range 0 to 1, r is generated.  If r less than the mutation rate, mr,  the mutation process will be carried out.processmutationmrr →<If  after  mutation  process,  the  sequence  of  assembly  or disassembly chromosomes did not follow the precedence rules, the mutation process is rejected.Step 5: Stopping conditionWhen the fitness of the chromosomes  is consistent,  it means that the best result has occurred. Then, the GA process will be terminated.4.0 RESULTS AND DISCUSSIONSTwo main results will be highlighted and discussed. First part concentrates  on  assembly  sequence  while  the  second  part focuses on disassembly sequence. 4.1 Assembly sequence resultsIn this experiment, it shows that by applying GA for a product with n components, the population undergoes most significant variation  in  an  early  searching  stage.  It  will  ends  after approximately n generation when it reaches a stable state. Fig 4 shows the fitness value for each generation. For this study, the program has been executed for 200 generations.crChromosome 1Chromosome 2After CrossoverChild 1Child 2Figure 4: Fitness value for each chromosomeBased  on  Fig  4,  it  shows  a  fluctuation  at  an  early  stage  in fitness  value of  each chromosome  but it  started  to be stable after few generations. The highest value of fitness (where it is stable) is considered to be the solution. In this case, it shows that  the  best  fitness  value  is  18.07991%.  The  solution  that produced the highest fitness value is shown below. Best Chromosome sequence based on part codes: • 1, 3, 2, 4, 6, 7, 5, 8, 16, 24, 14, 19, 17, 18, 13, 12, 9, 10, 11, 20, 21, 22, 23, 15, 25, 27, 26 • 1, 2, 3, 4, 5, 62, 19, 18, 17, 14, 24, 16, 8, 72, 12, 13, 10, 11, 9, 20, 21, 22, 23, 15, 25, 26, 27 • 1, 25, 26, 20, 9, 11, 10, 72, 12, 13, 4, 5, 62, 19, 18, 17, 14, 24, 16, 8, 3, 2, 22, 23, 15, 21, 27 Best All Chromosome sequence based on part codes: • 1, 3, 2, 4, 6, 7, 5, 8, 16, 24, 14, 19, 17, 18, 13, 12, 9, 10, 11, 20, 21, 22, 23, 15, 25, 27, 26The optimum solution or sequence produced from GA is then tested in DFA worksheet to see it accuracy in producing best result as manual  assembly in DFA. The time to assemble an iron using the sequence  produced  from GA is efficient  as it results in the minimum time among all assembly sequence that is  165.93  minutes.  On  the  other  hand,  the  traditional  DFA practice produced 174.03 minutes. 4.2 Disassembly sequence resultsSimilar  to  assembly  sequence,  the  population  searching underwent most significant  variation in early searching stage. It ended after approximately  n generation when it reached the stable  state.  Fig  5  and  6  show  the  fitness  value  for  each generation. For this study, 50 generations has been executed.Aver age Fi t ness of  GA1701711721731741751761771780 6 12 18 24 30 36 42 48Gener at i onsAverage FitnessAver age Fi t ness  Figure 5: Average fitness value for each chromosomeTot al  Fi t ness of  GA5100515052005250530053500 5 10 15 20 25 30 35 40 45Gener at i onTotal FitnessTot al  Fi t nessFigure 6: Total Fitness of GABased on Fig 5 and 6, the fitness of chromosomes decreased in the early stage. After that,  it started to be stable and reached static  condition.  This  mean  that  the  best  result  already occurred. The best fitness where it is stable is considered as the solution.  In this case,  it showed that  the best fitness value is 172.81  seconds,  while  manual  disassembly  time  is  about 171.16  seconds.  The  best  chromosome  that  produced  the highest fitness value is shown below and this is the optimum disassembly sequence.Best Chromosome sequence based on part codes• 1, 2, 3, 7, 8, 9, 10, 5, 11, 13, 12, 14, 15, 16, 17, 18, 6, 4, 19, 20, 22, 23, 21, 24, 25, 26, 275.0 CONCLUSIONAs  the  study  showed,  the  strength  of  GA  in  searching  an optimum  solution  had  been  proven.  Result  shows  a  near optimal solution is produced although the initial convergence began at early generations. However, based on the experiment, GA  does  not  guarantee  that  an  optimal  solution  can  be achieved  for  each  process.  The  main  reason  is  GA  used roulette  wheel  method  in  selecting  new  populations  in  this study. Although one chromosome has the highest fitness, there is no guarantee  that  it  will  be  selected.  On  the  average,  only  the chromosome  with  the  proportional  fitness  will  be  chosen. Thus,  this  GA  can  be  further  enhanced  with  other  AI techniques  to  obtain  a  more  efficient  selection  of  optimum assembly  and  disassembly  sequences.  Nevertheless,  the advantage of using this technique is that, it can be implemented at  the  design  process  and  can  obtain  faster  result  than  the traditional methods. REFERENCESHong, D.S and Cho, H.S. (1999).  A genetic-algorithm based approach to the generation of robotic assembly sequences. Journal of Control Engineering Practice, 7, pp. 151 – 159. Lazzerini,  B and Marcelloni,  F.  (2000).  A genetic  algorithm for  generating  optimal  assembly  plans.  Journal  of  Artificial Intelligence in Engineering, 14, pp. 319 – 329. 

Product assembly and disassembly sequence optimization based on genetic algorithm and design for assembly methodologies

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Product assembly and disassembly sequence optimization based on genetic algorithm and design for assembly methodologies

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