Simultaneous optimal selection of design and manufacturing tolerances with different stack-up conditions using genetic algorithms

Singh, Premlata; Jain, Pramod Kumar; Jain, S. C.

doi:10.1080/0020754031000087328

Cited by 58 publications

(40 citation statements)

References 28 publications

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“…The methodology was demonstrated with the help of a simple linear assembly considering different tolerance stack-up conditions. Apart from the traditional worst case and RSS criteria, two non-traditional approaches, Spotts' modified and Greenwood and Chase's estimated mean shift stack-up conditions were explored [13,14,15] . Dimitrellou [5] presented a method for the automatic establishment and processing of machine shop and application specific cost-tolerance functions for the cost-optimum allocation of tolerances.…”

Section: Literature Reviewmentioning

confidence: 99%

Calculation of Total Cost, Tolerance Based on Taguchiâ€™s, AsymmetricQuality Loss Function Approach

Kumar¹,

Alagumurthi²,

Ramesh³

2009

American J. of Engineering and Applied Sciences

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Problem statement: Current world market force the manufacturing sectors to develop high quality product and process design with minimum possible cost. About 80% of problems in production units may be attributed to 20% of design tolerance causes. While design typically represents the smallest actual cost elements in products (around 5%), it leverages the largest cost influence (around 70%). So design engineers continuously stumble upon problem of design for high quality performance with lower cost. Objectives of this study where to: (i) simultaneous selection of design and manufacturing tolerance (ii) minimization of total cost (sum of the manufacturing cost and Taguchi's asymmetric quality cost) (iii) minimum cost and its machining tolerance. Approach: Rotor key base assembly was considered as case study to optimize the minimization of assembly total cost and machining tolerance. New global nonlinear optimization techniques called pattern search algorithm had been implemented to find optimal tolerance allocation and total cost. Results: In this study minimum cost arrived was 45.15 Cr and its corresponding tolerances for machining process turning, drilling, face milling, face milling and drilling where 0.063, 0.0508, 0.2127, 0.2127, 0.2540 mm respectively at worst case conditions. Conclusion: Results indicated that optimization by integer programming, sequential quadratic programming and exhaustive search, nonlinear programming, genetic algorithm, simulated annealing, fuzzy logic, number set theory and Monte Carlo simulation did not give much least total cost and also predicted that pattern search algorithm was robust method. Second the method, generally termed as concurrent tolerance synthesis was well suited for engineering environment, where high quality products with low total cost were designed and manufactured.

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Section: Literature Reviewmentioning

confidence: 99%

Calculation of Total Cost, Tolerance Based on Taguchiâ€™s, AsymmetricQuality Loss Function Approach

Kumar¹,

Alagumurthi²,

Ramesh³

2009

American J. of Engineering and Applied Sciences

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“…The general steps to be followed for implementing [12] simple GA for optimization Problems are listed below. However, note that they can be modified appropriately depending on the type and complexity of the problem.…”

Section: Working Of Genetic Algorithmmentioning

confidence: 99%

Integrated total cost and Tolerance Optimization with Genetic Algorithm

Kumar¹,

Alagumurthi²

2010

IJCIS

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The tolerance allocation problem has been studied in the literature for decades, usually using mathematical programming (or) heuristic optimization approaches. Elegant tools for minimum cost tolerance allocation have been developed over several decades but still there is no specified tool to find the total cost with respect to machining cost and asymmetric quality loss functions. Objective of this paper is to find the optimized total cost by considering the machining cost and the asymmetric quality loss of an assembly. The non linear multi variable optimization of total cost problem formulated is solved using genetic algorithm. In this work, Piston and cylinder arrangement example was considered for concurrent allocation of design and machining tolerance, as well as to optimize the total cost. The result shows, optimized tolerance will enhances the quality and reduces the total cost

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“…The manufacturing decision is the total machining cost and is determined by summing the machining cost-tolerance model as Equation 1 and Equation 2 subjecting to the constraints and ranges of the principal design and machining tolerances. The constant parameters are the same as in (Singh et al, 2003 The proposed PSO algorithm with special constraints handling strategy was used to solve this problem. To validate its efficiency, this new approach was compared with GA in (Singh et al, 2003).…”

Section: Design Examplementioning

confidence: 99%

“…Four stack-up conditions (Singh et al, 2003) (worst case, RSS, Spotts' modified method and estimated mean shift criteria) are employed to formulate the corresponding constraints. (2)The machining tolerance parameters are: ij where i=1,2 and j=1,2,3,4.…”

Section: Design Examplementioning

confidence: 99%

“…Zhang (Zhang, 1996) presented the concept of simultaneous tolerance, proposed a general mathematical model for tolerance optimization in concurrent engineering context, and then introduced a new concept of interim tolerances that help determine appropriate manufacturing processes. Singh (Singh et al, 2003) utilized genetic algorithms and penalty function approach to solve the problem of simultaneous selection of design and manufacturing tolerances based on the minimization of the total manufacturing cost. Gao and Huang (Gao & Huang, 2003) utilized a nonlinear programming model for optimal process tolerance simultaneously based on the objective of total manufacturing cost with different weighting factors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization for Simultaneous Optimization of Design and Machining Tolerances

Zhou

Gao

et al. 2007

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

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Simultaneous optimal selection of design and manufacturing tolerances with different stack-up conditions using genetic algorithms

Cited by 58 publications

References 28 publications

Calculation of Total Cost, Tolerance Based on Taguchiâ€™s, AsymmetricQuality Loss Function Approach

Calculation of Total Cost, Tolerance Based on Taguchiâ€™s, AsymmetricQuality Loss Function Approach

Integrated total cost and Tolerance Optimization with Genetic Algorithm

Particle Swarm Optimization for Simultaneous Optimization of Design and Machining Tolerances

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