A Closed-Form Approach for Optimum Tolerance Allocation of Assemblies with General Tolerance-Cost Function

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Tolerancing is one of the most crucial foundations for industry development and an index of product quality and cost. As tolerance allocation is based on manufacturing costs, this paper proposes a comprehensive method for optimal tolerance allocation with minimum manufacturing cost subject to constraints on dimensional chains and machining capabilities. The general reciprocal power and exponential cost-tolerance models with equality constraints as well as the worst-case and statistical tolerancings are employed in this method. A closed-form solution for the optimization problem by applying Lagrange multipliers is derived. The optimal tolerance allocation problem for reciprocal exponential cost-tolerance model by introducing Lambert W function is demonstrated. For constrained minimization problems with only equality constraints, the optimum design can be obtained by solving simultaneous equations without differentiating. An example is illustrated to demonstrate this approach. The result also shows that tolerance can be allocated economically and accurately using this method. The contribution of this paper is to solve the optimal tolerancing allocation problem by an efficient and robust method with simultaneous active constraints.

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Advanced Materials Research (Volumes 201-203)

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1272-1278

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February 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Z. Dong, In: Advanced Tolerancing Techniques, Chapter 9, pp.233-244, Zhang, H.C. (Eds. ), New York (1997).

Google Scholar

[2] S.H. Yeo, B.K.A. Ngoi, L.S. Poh and C. Hang: International Journal of Advanced Manufacturing Technology, Vol. 13 (1997), pp.35-41.

Google Scholar

[3] K.W. Chase, W.H. Greenwood, B.G. Loosli and L.F. Hauglund: Manufacturing Review, ASME, Vol. 3, No. 1 (1990), pp.49-59.

Google Scholar

[4] M.S. Chen: International Journal of Advanced Manufacturing Technology, Vol. 12, No. 5 (1996), pp.349-355.

Google Scholar

[5] C.C. Yang and V.N.A. Naikan: International Journal of Production Economics, Vol. 84 (2003), pp.149-163.

Google Scholar

[6] C.C. Yang and V.N.A. Naikan: Computers & Industrial Engineering, Vol. 45 (2003), pp.511-543.

Google Scholar

[7] P.K. Singh, P.K. Jain and S.C. Jain: Proceedings of the Institution of Mechanical Engineers. Part B: Journal of engineering manufacture, Vol. 218 (2004), pp.765-778.

Google Scholar

[8] M.S. Kumar, S.M. Kannan and V. Jayabalan: International Journal of Production Research, Vol. 45, No. 6 (2007), pp.1449-1468.

Google Scholar

[9] M.S. Kumar, S.M. Kannan and V. Jayabalan: International Journal of Advanced Manufacturing Technology, Vol. 40 (2009), pp.819-836.

Google Scholar

[10] M.S. Kumar, M.N. Islam, N. Lenin and D.V. Kumar: International Journal of Engineering, Vol. 3, No. 4 (2009), pp.380-402.

Google Scholar

[11] M.S. Kumar and B. Stalin: International Journal of Advanced Manufacturing Technology, Vol. 44 (2009), pp.405-411.

Google Scholar

[12] J.S. Arora, in: Introduction to Optimum Design, Chapter 3, pp.93-148, Elsevier Academic Press, 2nd Edition, Oxford, UK (2004).

Google Scholar

[13] R.M. Corless, G.H. Gonnet, D.E.G. Hare, D.J. Jeffrey and D.H. Knuth: Advances in Computation Mathematics, Vol. 5 (1996), pp.329-359.

Google Scholar

[14] S. Shin, M.S. Govindaluri and B.R. Cho: Quality and Reliability Engineering International, Vol. 21, No. 8 (2005), pp.795-808.

Google Scholar

[15] R. Roy and F.W.J. Olver, In: NIST Handbook of Mathematical Functions, Chapter 4, p.111, Cambridge University Press (2010).

Google Scholar