Novel cost–tolerance model based on fuzzy neural networks

Abstract

The conventional cost–tolerance model is constructed by linear or non-linear regression analysis based upon empirical data from all frequently used production processes. These approaches suffer relatively large model-fitting errors and also fail to consider the varying manufacturing environment due to the simplicity of the mathematical models used. In the present study, a novel, multi-parameter cost–tolerance model is developed based on a fuzzy neural network (FNN) that has tolerance and a cost influence coefficient as inputs and manufacturing cost as output. First, the cost influence coefficient is introduced based on analysis of the manufacturing environment. Then, a multi-input, FNN-based cost–tolerance model is constructed. Analysis of the model is conducted and comparison is made with existing models. Analytical results show that the proposed model yields better performance in controlling the average fitting error and is flexible to varying manufacturing environment. It can provide more reliable results and reduce the chances of error in the tolerance design.

Keywords

cost–tolerance model cost influence coefficient fuzzy neural network manufacturing environment

References

Yang

Computer aided tolerance design 1999(Zhejiang University Press Hangzhou, China)

Cao

Study on the methodology and technology of robust tolerance design for manufacture PhD Thesis, Department of Mechanical Engineering, Zhejiang University, 2003

Singh

P. K.

Jain

P. K.

Jain

S. C.

Important issues in tolerance design of mechanical assemblies. Part 1: Tolerance analysis. Proc. IMechE, Part B: J. Engineering Manufacture 2009, 223 (10), 1225–1247 DOI: 10.1243/09544054JEM1304A

Singh

P. K.

Jain

P. K.

Jain

S. C.

Important issues in tolerance design of mechanical assemblies. Part 2: Tolerance synthesis. Proc. IMechE, Part B: J. Engineering Manufacture 2009, 223 (10), 1249–1287 DOI: 10.1243/09544054JEM1304B

Verlinden

Duflou

J. R.

Collin

Cattrysse

Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study. Int. J. Prod. Econ. 2008, 111 (2), 484–492

Speckhart

F. H.

Calculation of tolerance based on a minimum cost approach. ASME J. Engng Ind. 1972, 94 (2), 447–453

Spotts

Allocation of tolerances to minimize cost of assembly. ASME J. Engng Ind. 1973, 95 (3), 762–764

Sutherland

Roth

Mechanism design: Accounting for manufacturing tolerances and costs in function generating problems. ASME J. Engng Ind. 1975, 97 (1), 283–286

Elmaraghy

Evaluation of cost–tolerance algorithms for design tolerance analysis and synthesis. Mfg Rev. 1988, 1 (3), 168–179

10.

Dong

Xue

New production cost–tolerance models for tolerance synthesis. ASME J. Engng Ind. 1994, 116 (3), 199–206

11.

Yeo

Ngoi

Chen

A cost–tolerance model for process sequence optimisation. Int. J. Adv. Mfg Technol. 1996, 12 (6), 423–431

12.

Yang

Machining cost–tolerance model based on artificial neural network. Chin. Mech. Engng 1996, 7 (6), 41–42

13.

Chen

Tolerance synthesis by neural learning and nonlinear programming. Int. J. Prod. Econ. 2001, 70 (1), 55–65

14.

Lin

Chang

Cost–tolerance analysis model based on a neural networks method. Int. J. Prod. Res. 2002, 40 (6), 1429–1452

15.

Wang

Artificial neural networks as cost engineering methods in a collaborative manufacturing environment. Int. J. Prod. Econ. 2007, 109 (1–2), 53–64

16.

Shehab

E. M.

Abdalla

H. S.

Manufacturing cost modelling for concurrent product development. Robot. Comput. Integr. Mfg 2001, 17 (4), 341–353

17.

Wang

Fuzzy system, fuzzy neural networks and the design of application program 1997(Shanghai Scientific & Technical Press, Shanghai, China)

18.

Jia

Shan

Liu

Niu

Fuzzy neural model for flatness pattern recognition. J. Iron Steel Res. Int. 2008, 15 (6), 33–38

19.

Liano

Robust error measure for supervised neural network learning with outliers. IEEE Trans. Neural Network. 1996, 7 (1), 246–250

20.

Mohanraj

Jayaraj

Muraleedharan

Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks. Appl. Energ. 2009, 86 (9), 1442–1449

21.

Wang

Zhang

Kong

Investigation into cost–tolerance model based on varying manufacture environment. J. Taiyuan Heavy Mach. Inst. 2001, 22 (3), 204–207