Abstract
This paper presents a generic optimisation methodology for the selection of optimal process parameters in multiresponse processes that consists of two stages: The first stage is based on a quality loss function and multivariate statistical methods in order to adequately present responses, uncorrelate and synthesise them into a single process performance measure; the second stage uses artificial neural networks to build a process model and a simulated annealing (SA) algorithm to find the optimal process parameter conditions. The initial point of the SA algorithm is generated in such a way as to improve the convergence to the actual global optimum. The most important SA algorithm parameters are varied to assess the algorithm’s robustness in terms of the effect of the algorithm setting on the quality of the final solution (optimal process parameters and the corresponding process performance measure). The results are benchmarked to genetic algorithm (GA) performance within the proposed methodology, and the quality of a final solution, the robustness of the optimisation algorithm, the speed of a convergence to the optimum and the computational time are evaluated. Four case studies are presented to illustrate the effectiveness of the proposed methodology in comparison to several commonly used approaches from the literature and also to the GA-based performance.
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References
Mukherjee I, Ray PK (2006) A review of optimization techniques in metal cutting processes. Computers and Industrial Engineering 50(1):15–34. doi:10.1016/j.cie.2005.10.00
Taguchi G (1986) Introduction to quality engineering. Asian Productivity Organization, UNIPUB, New York
Antony J (2001) Simultaneous optimization of multiple quality characteristics in manufacturing processes using Taguchi’s quality loss function. Int J Adv Manuf Technol 17(2):134–138. doi:10.1007/s001700170201
Yang C, Hung CW (2004) Optimising the thermoforming process of polymeric foams: an approach by using the Taguchi method and the utility concept. Int J Adv Manuf Technol 24(5–6):353–360. doi:10.1007/s00170-003-1591-4
Tong L-I, Wang C-H, Chen H-C (2005) Optimization of multiple responses using principal component analysis and, technique for order preference by similarity to ideal solution. Int J Adv Manuf Technol 27(3–4):407–414. doi:10.1007/s00170-004-2157-9
Liao HC (2003) Using PCR-TOPSIS to optimise Taguchi’s multi-response problem. Int J Adv Manuf Technol 22(9–10):649–655. doi:10.1007/s00170-002-1485-x
Su CT, Tong LI (1997) Multi-response robust design by principal component analysis. Total Quality Management 8(6):409–416
Fung CP, Kang PC (2005) Multi-response optimization in friction properties of PBT composites using Taguchi method and principle component analysis. Journal of Materials Processing Technology 170(3):602–610. doi:10.1016/j.jmatprotec.2005.06.040
Liao HC (2006) Multi-response optimization using weighted principal component. Int J Adv Manuf Technol 27(7–8):720–725. doi:10.1007/s00170-004-2248-7
Chang CS, Liao RC, Wen KL, Wang WP (2004) A grey-based Taguchi method to optimize design of muzzle flash restraint device. Int J Adv Manuf Technol 24(11–12):860–864. doi:10.1007/s00170-004-2066-y
Lin CT, Chang CW, Chen CB (2006) A simple approach to solving multi-response quality characteristic problems in CMOS ion implantation. Int J Adv Manuf Technol 28(5–6):592–595. doi:10.1007/s00170-004-2396-9
Wu CF (2002) Optimisation of multiple quality characteristics based on percentage reduction of Taguchi’s quality loss. Int J Adv Manuf Technol 20(10):749–753. doi:10.1007/s001700200233
Govindaluri SM, Cho BR (2007) Robust design modeling with correlated quality characteristics using a multicriteria decision framework. Int J Adv Manuf Technol 32(5–6):423–433. doi:10.1007/s00170-005-0349-6
Myers RH, Montgomery DC (2002) Response surface methodology: process and product optimization using designed experiments. Wiley, New York
Dhara SK, Kuar AS, Mitra S (2008) An artificial neural network approach on parametric optimization of laser micro-machining of die-steel. Int J Adv Manuf Technol 39(1–2):39–46. doi:10.1007/s00170-007-1199-1
Hsu C-M, Su C-T, Liao D (2004) Simultaneous optimisation of the broadband tap coupler optical performance based on neural networks and exponential desirability functions. Int J Adv Manuf Technol 23(11–12):896–902. doi:10.1007/s00170-003-1733-8
Khoo LP, Chen CH (2001) Integration of response surface methodology with genetic algorithms. Int J Adv Manuf Technol 18(7):483–489. doi:10.1007/s0017010180483
Hou T-H, Chen S-H, Lin T-Y, Huang K-M (2006) An integrated system for setting the optimal parameters in IC chip-package wire bonding processes. Int J Adv Manuf Technol 30(3–4):247–253. doi:10.1007/s00170-005-0083-0
Tong KW, Kwong CK, Yu KM (2004) Process optimisation of transfer moulding for electronic packages using artificial neural networks and multiobjective optimisation techniques. Int J Adv Manuf Technol 24(9–10):675–685. doi:10.1007/s00170-003-1739-2
Huang CC, Tang TT (2006) Parameter optimization in melt spinning by neural networks and genetic algorithms. Int J Adv Manuf Technol 27(1–2):1113–1118. doi:10.1007/s00170-004-2302-5
Yu J-C, Chen X-X, Hung T-R, Thibault F (2004) Optimization of extrusion blow molding processes using soft computing and Taguchi’s method. Journal of Intelligent Manufacturing 15(5):625–634. doi:10.1023/B:JIMS.0000037712.33636.41
Chen W-C, Fu G-L, Tai P-H, Deng W-J (2009) Process parameter optimization for MIMO plastic injection molding via soft computing. Expert Systems with Application 36(2):1114–1122. doi:10.1016/j.eswa.2007.10.020
Sathiya P, Aravindan S, Noorul Haq A, Paneerselvam K (2009) Optimization of friction welding parameters using evolutionary computational techniques. Journal of Materials Processing Technology 209(5):2576–2584. doi:10.1016/j.jmatprotec.2008.06.030
Chen H-C, Lin J-C, Yang Y-K, Tsai C-H (2010) Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach. Expert Systems with Applications 37(10):7147–7153. doi:10.1016/j.eswa.2010.04.020
Yang S-H, Srinivas J, Mohan S, Lee D-M, Balaji S (2009) Optimization of electric discharge machining using simulated annealing. Journal of Materials Processing Technology 209(9):4471–4475. doi:10.1016/j.jmatprotec.2008.10.053
Saravanan R, Asokan P, Vijayakumar K (2003) Machining parameters optimisation for turning cylindrical stock into a continuous finished profile using genetic algorithm (GA) and simulated annealing (SA). Int J Adv Manuf Technol 21:1–9
Chang H-H (2008) A data mining approach to dynamic multiple responses in Taguchi experimental design. Expert Systems with Applications 35(3):1095–1103. doi:10.1016/j.eswa.2007.08.005
Mukherjee I, Ray PK (2008) Optimal process design of two-stage multiple responses grinding processes using desirability functions and metaheuristic technique. Applied Soft Computing 8(1):402–421. doi:10.1016/j.asoc.2007.02.005
Suresh RK, Mohanasundaram KM (2006) Pareto archived simulated annealing for job shop scheduling with multiple objectives. Int J Adv Manuf Technol 29:184–196. doi:10.1007/s00170-004-2492-x
Ganesh K, Punniyamoorthy M (2005) Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing. Int J Adv Manuf Technol 26:148–154. doi:10.1007/s00170-003-1976-4
Nallakumarasamy G, Srinivasan PSS, Venkatesh RK, Malayalamurthi R (2011) Optimization of operation sequencing in CAPP using simulated annealing technique (SAT). Int J Adv Manuf Technol 54:721–728. doi:10.1007/s00170-010-2977-8
Pandey V, Tiwari MK, Kumar S (2006) An interactive approach to solve the operation sequencing problem using simulated annealing. Int J Adv Manuf Technol 29:1212–1231. doi:10.1007/s00170-005-0007-z
Arkat J, Saidi M, Abbasi B (2007) Applying simulated annealing to cellular manufacturing system design. Int J Adv Manuf Technol 32:531–536. doi:10.1007/s00170-005-0358-5
Zhao F, Hong Y, Yu D, Yang Y, Zhang Q, Yi H (2007) A hybrid algorithm based on particle swarm optimization and simulated annealing to holon task allocation for holonic manufacturing system. Int J Adv Manuf Technol 32:1021–1032. doi:10.1007/s00170-006-0418-5
Zandieh M, Amiri M, Vahdani B, Soltani R (2009) A robust parameter design for multi-response problems. Journal of Computational and Applied Mathematics 230(2):463–476. doi:10.1016/j.cam.2008.12.019
Sibalija T, Majstorovic V, Miljkovic Z (2011) An intelligent approach to robust multiresponse process design. Int J Prod Res 49(17):5079–5097. doi:10.1080/00207543.2010.511476
Sibalija T, Petronic S, Majstorovic V, Prokic-Cvetkovic R, Milosavljevic A (2011) Multi-response design of Nd:YAG laser drilling of Ni-based superalloy sheets using Taguchi’s quality loss function, multivariate statistical methods and artificial intelligence. Int J Adv Manuf Technol 54:537–552. doi:10.1007/s00170-010-2945-3
Sibalija T., Majstorovic V (2011) An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0451-y
Šibalija T, Majstorović V, Soković M (2011) Taguchi-based and intelligent optimisation of a multi-response process using historical data. Strojniški vestnik—Journal of Mechanical Engineering 57(4):357–365. doi:10.5545/sv-jme.2010.061
Spall J (2003) Introduction to stochastic search and optimisation. Wiley, New Jersey
Sukthomya W, Tannock JDT (2005) Taguchi experimental design for manufacturing process optimization using historical data and neural network process model. International Journal of Quality & Reliability Management 22(5):485–502. doi:10.1108/02656710510598393
Jeong B, Lee J, Cho H (2005) Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm. International Journal of Production Research 43(15):3209–3230. doi:10.1080/00958970500137167
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Sibalija, T.V., Majstorovic, V.D. An integrated simulated annealing-based method for robust multiresponse process optimisation. Int J Adv Manuf Technol 59, 1227–1244 (2012). https://doi.org/10.1007/s00170-011-3572-3
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DOI: https://doi.org/10.1007/s00170-011-3572-3