A proper scheme of fixture layout can reduce locating errors of a workpiece. However, a multi-objective fixture layout optimization, involving multiple conflicting objective functions, will lead to much more accurate solution and complex computation. This paper describes a new approach based on Memetic Algorithm (MA) to multi-objective fixture layout optimization, considering both location accuracy and stability. This approach uses an entropy method to obtain the weights of each objective functions, and establishes a fitness function. Then, a MA is developed to properly select the positions of locators to minimize the fitness function. An example illustrates that the new optimization method based on MA can improve the locating precision of a workpiece effectively and efficiently. This method is also suitable for solving a multi-objective fixture layout optimization problem with more objective functions.
AsadaH. Kinematic analysis of workpart fixturing for flexible assembly with automatically reconfigurable fixtures. IEEE J Robot Automat1985; 1: 86–94.
2.
WangMYPelinescuDM. Optimizing fixture layout in a point-set domain. IEEE Transact Robot Automat2001; 17: 312–323.
3.
QinGHZhangWH. Generalized modeling and analysis techniques of fixture locating errors on linear dimensions based on Kinematics. Mech Sci Technol2005; 23: 1317–1319.
4.
QinGHZhangWHWanM. A mathematical approach to analysis and optimal design of a fixture locating scheme. Int J Adv Manuf Technol2006; 29: 349–359.
5.
WangYChenXLiuQ. Optimisation of machining fixture layout under multi-constraints. Int J Mach Tools Manuf2006; 46: 1291–1300.
6.
TieJWLouPHQinGH. Novel approach to locator layout optimization based on genetic algorithm. Transact Nanjing Univ Aeronaut Astronaut2011; 2: 176–182.
NiLJChenWF. Multi-objective fixture scheme optimization and alterable clamping force optimization. Machine Des Manuf2007; (7): 7–9.
9.
JiangKZhouXLiM. A multi-objective optimization and decision algorithm for locator layout continuous searching in checking fixture design. Int J Adv Manuf Technol2013; 67: 357–366.
10.
Moscato P. On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 1989.
MolinaDLozanoMGarcía-MartínezC. Memetic algorithms for continuous optimisation based on local search chains. Evolution Computat2010; 18: 27–63.
13.
WangHWangDYangS. A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput2009; 13: 763–780.
14.
AlmeidaLMLudermirTB. A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks. Neurocomputing2010; 73: 1438–1450.
15.
UrselmannMSandGEngellS. A memetic algorithm for global optimization in chemical process synthesis. IEEE Congress Evolution Computat2009, pp. 1721–1728.
ZhangHGuCGuL. The evaluation of tourism destination competitiveness by TOPSIS & information entropy–A case in the Yangtze River Delta of China. Tourism Manage2011; 32: 443–451.
18.
KayaN. Machining fixture locating and clamping position optimization using genetic algorithms. Comput Indus2006; 57: 112–120.
19.
MarinRAFerreiraPM. Kinematic analysis and synthesis of deterministic 3-2-1 locator schemes for machining fixtures. J Manuf Sci Eng2001; 123: 708–719.
20.
WangMY. An optimal design for 3D fixture synthesis in a point set domain. IEEE Trans Robot Automat2000; 16: 833–841.
21.
El-SantawyMFAhmedAN. An information Entropy Weighting method combined to TOPSIS approach for ranking consulting firms. Life Sci J2012; 9: 147–150.
22.
ZelenyM. Multiple criteria decision making, New York: McGraw-Hill, 1982.
23.
WangTCLeeHD. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Syst Appl2009; 36: 8980–8985.
24.
Orike S and Corne DW. A memetic algorithm for dynamic economic load dispatch optimization. In: IEEE symposium on computational intelligence in dynamic and uncertain environments (CIDUE), Singapore, 16–19 April 2013, pp.92–99.
25.
Qin GH, Zhang W and Wan MA. Machining-dimension based approach to locating scheme design. Journal of Manufacturing Science and Engineering 2008; 130: 051010-1–051010-7.
26.
VishnupriyanSMajumderMCRamachandranK. Optimization of machining fixture layout for tolerance requirements under the influence of locating errors. Int J Eng Sci Technol2010; 2: 152–161.
