Abstract
To maintain optimal quality characteristics in the defined specification limits is a vital decision for any industry and service system. To avoid nonconformity in outputs, the stream of variations and their potential causes must be identified so that the response variables fall into desirable limits across the manufacturing or service chain. Response surface methodology is considered as a powerful technique to facilitate the analysis of the mentioned problem. This paper presents the general quality chain design problem as a mathematical program and also proposes a method to solve it using multiple response surface methodology. An example of multistage processes is analyzed by the proposed approach to show its efficacies numerically and analytically.
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Shi J, Zhou S (2009) Quality control and improvement for multistage systems: a survey. IIE Trans 41(9):744–753
Phoomboplab T, Ceglarek D (2008) Process yield improvement through optimum design of fixture layouts in 3D multistation assembly systems. J Manuf Sci Eng 130(6):0610051–06100517
Huang W, Lin J, Kong Z, Ceglarek D (2007) Stream-of-variation (SOVA) modeling—part II: a generic 3D variation model for rigid body assembly in multistation assembly processes. J Manuf Sci Eng 129(4):832–842
Ding Y, Ceglarek D, Shi J (2000) Modeling and diagnosis of multi-stage manufacturing process: part I—state space model. Japan-USA Symposium of Flexible Automation
Liu J, Shi J, Hu SJ (2009) Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes. IIE Trans 41(4):323–334
Zeng L, Zhou S (2007) Inferring the interactions in complex manufacturing processes using graphical models. Technometrics 49(4):373–381
Li J, Shi J (2007) Knowledge discovery from observational data for process control using causal Bayesian networks. IIE Trans 39(6):681–690
Zou C, Tsung F, Liu Y (2008) A change point approach for phase I analysis in multistage processes. Technometrics 50(3):344–356
Zou C, Tsung F (2008) Directional MEWMA schemes for multistage process monitoring and diagnosis. J Qual Tech 40(4):407–427
Xiang L, Tsung F (2008) Statistical monitoring of multi-stage processes based on engineering models. IIE Trans 40(10):957–970
Li Y, Tsung F (2009) False discovery rate-adjusted charting schemes for multistage process monitoring and fault identification. Technometrics 51(2):186–205
Mandroli SS, Shrivastava AK, Ding Y (2006) A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes. IIE Trans 38(4):309–328
Chen Y, Ding Y, Jin J, Ceglarek D (2006) Integration of process-oriented tolerancing and maintenance planning in design of multistation manufacturing processes. IEEE Trans Autom Sci Eng 3(4):440–453
Cheng KM, Tsai JC (2011) Optimal statistical tolerance allocation of assemblies for minimum manufacturing cost. Appl Mech Mater 52–54:1818–1823
Huang YM, Shiau CS (2009) An optimal tolerance allocation model for assemblies with consideration of manufacturing cost, quality loss and reliability index. Assemb Autom 29(3):220–229
Hung TC, Chan KY (2011) Multi-objective design and tolerance allocation for single- and multi-level systems. J Intell Manuf. doi:10.1007/s10845-011-0608-3, pp. 1–15
Shin S, Kongsuwon P, Cho BR (2010) Development of the parametric tolerance modeling and optimization schemes and cost-effective solutions. Eur J Oper Res 207(3):1728–1741
Huang W, Phoomboplab T, Ceglarek D (2009) Process capability surrogate model-based tolerance synthesis for multi-station manufacturing systems. IIE Trans 41(4):309–322
Wazed MA, Ahmed S, Nukman Y (2011) Application of Taguchi method to analyse the impacts of common process and batch size in multistage production system under uncertain conditions. Eur J Industrial Eng 5(2):215–231
Lee BY, Tarng YS (2000) Cutting-parameter selection for maximizing production rate or minimizing production cost in multistage turning operations. J Mater Process Tech 105(1–2):61–66
Mukherjee I, Ray PK (2008) Optimal process design of two-stage multiple responses grinding processes using desirability functions and metaheuristic technique. Appl Soft Comput 8(1):402–421
Mukherjee I, Ray PK (2009) Quality improvement of multistage and multi-response grinding processes: an insight into two different methodologies for parameter optimisation. Int J Prod Qual Manag 4(5):613–643
Mukherjee I, Ray PK (2012) Ascendancy of a modified tabu search for multi-stage non-linear multiple response constrained optimisation problem. Int J Prod Qual Manag 9(3):352–381
Kim K-J, Lin DKJ (2000) Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions. J Roy Stat Soc C Appl Stat 49(3):311–325. doi:10.1111/1467-9876.00194
Jr. Harrington EC (1965) Desirability function. Ind Qual Contol 21(10):494–498
Derringer GC, Suich R (1980) Simultaneous-optimization of several response variables. J Qual Tech 12(4):214–219
Vining GG (1998) A compromise approach to multiresponse optimization. J Qual Tech 30(4):309–313
Pignatiello JJ (1993) Strategies for robust multiresponse quality engineering. IIE Trans 25(3):5–15
Khuri AI, Conlon M (1981) Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics 23(4):363–375
Chiao CH, Hamada M (2001) Analyzing experiments with correlated multiple responses. J Qual Tech 33(4):451–465
Bashiri M, Hejazi TH (2009) An extension of multi-response optimization in MADM view. J Appl Sci 9(9):1695–1702
Kazemzadeh RB, Bashiri M, Atkinson AC, Noorossana R (2008) A general framework for multiresponse optimization problems based on goal programming. Eur J Oper Res 189(2):421–429. doi:10.1016/j.ejor.2007.05.030
Hejazi TH, Bashiri M, Díaz-García JA, Noghondarian K (2012) Optimization of probabilistic multiple response surfaces. Appl Math Model 36(3):1275–1285
Abedini V, Shakeri M, Siahmargouie MH (2012) Error analysis in multistage machining process using kinematic analysis of workpiece fixturing. Appl Mech Mater 152–154:430–435
Li Y, Tsung F (2011) Detecting and diagnosing covariance matrix changes in multistage processes. IIE Trans 43(4):259–274
Colledani M, Tolio T (2012) Integrated quality, production logistics and maintenance analysis of multi-stage asynchronous manufacturing systems with degrading machines. CIRP Annals—Manufacturing Technology 61(1):455–458
Zantek PF, Wright GP, Plante RD (2006) A self-starting procedure for monitoring process quality in multistage manufacturing systems. IIE Trans 38(4):293–308
Rotondo A, Young P, Geraghty J (2012) Quality risk prediction at a non-sampling station machine in a multi-product, multi-stage, parallel processing manufacturing system subjected to sequence disorder and multiple stream effects. Ann Oper Res. doi:10.1007/s10479-012-1145-y, pp. 1–23
Niaki STA, Davoodi M (2008) Designing a multivariate–multistage quality control system using artificial neural networks. Int J Prod Res 47(1):251–271
Hejazi TH, Bashiri M, Noghondarian K, Atkinson AC (2011) Multiresponse optimization with consideration of probabilistic covariates. Qual Reliab Eng Int 27(4):437–449. doi:10.1002/qre.1133
Salmasnia A, Baradaran Kazemzadeh R, Seyyed-Esfahani M, Hejazi TH (2013) Multiple response surface optimization with correlated data. Int J Adv Manuf Tech 64(5–8):841–855. doi:10.1007/s00170-012-4056-9
Taguchi G, Chowdhury S, Wu Y, Taguchi S, Yano H (2005) Taguchi’s quality engineering handbook. John Wiley & Sons; ASI Consulting Group, Hoboken- N.J.Livonia, Mich
Montgomery DC (2005) Design and analysis of experiments, 6th edn. Wiley, Hoboken
Shah HK, Montgomery DC, Carlyle WM (2004) Response surface modeling and optimization in multiresponse experiments using seemingly unrelated regressions. Qual Eng 16(3):387–397
Kamali Nejad M, Vignat F, Villeneuve F (2012) Tolerance analysis in machining using the model of manufactured part (MMP)—Comparison and evaluation of three different approaches. Int J Comput Integrated Manuf 25(2):136–149
Zellner A (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J Am Stat Assoc 57(298):348–368
Albert A (1972) Regression and the Moore-Penrose pseudoinverse, 1st edn. Academic Press, Waltham
Penrose R (1955) A generalized inverse for matrices. Math Proc Camb Phil Soc 51(03):406–413
Dresden A (1920) The fourteenth western meeting of the American Mathematical Society. Bull Am Math Soc 26(9):385–396
Giri NC (1977) Multivariate statistical inference. Academic Press, Waltham
MATLAB (2011). version 7.13.0.564 (2011b) edn. The MathWorks Inc., Natick, Massachusetts.
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Hejazi, T.H., Seyyed-Esfahani, M. & Mahootchi, M. Quality chain design and optimization by multiple response surface methodology. Int J Adv Manuf Technol 68, 881–893 (2013). https://doi.org/10.1007/s00170-013-4950-9
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DOI: https://doi.org/10.1007/s00170-013-4950-9