Optimal tolerance allocation using a multiobjective particle swarm optimizer

Forouraghi, Babak

doi:10.1007/s00170-008-1892-8

Cited by 31 publications

(8 citation statements)

References 18 publications

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“…al [8] also present an optimal tolerance allocation method on a statistical model. Another allocation method based on optimization is proposed by Forouraghi [14] using a special multi-objective particle swarm optimizer. Loof et.…”

Section: Prior Workmentioning

confidence: 99%

Statistical geometric computation on tolerances for dimensioning

Keyser

2016

Computer-Aided Design

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Section: Prior Workmentioning

confidence: 99%

Statistical geometric computation on tolerances for dimensioning

Keyser

2016

Computer-Aided Design

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“…(Part 1 and Part 2) 14,15 presented the systematic approach of tolerance analysis and synthesis for solving the design problem with one- and two-dimensional assemblies. Forouraghi 16 demonstrated particle swarm optimization (PSO) for solving multi-objective design problems of stepper bar assembly and compared the results with existing traditional calculus-based method and showed that the proposed method outperforms the traditional methods. Karaboga et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Tolerance allocation of complex assembly with nominal dimension selection using Artificial Bee Colony algorithm

Kumar¹,

Ravindran²,

Nagarajan

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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Optimum tolerance allocation plays a vital role in minimizing the direct manufacturing cost of mechanical assembly. It is very sensitive due to the variations in manufacturing processes of the components. Most of the earlier studies are aiming at optimum tolerance allocation for assemblies without considering the selection of nominal dimensions of components and considering them as discrete values. It is proposed to minimize the manufacturing cost of an assembly with tolerance allocation and alternate nominal dimension selections by considering them in closer decimal intervals. The evolutionary algorithms such as Genetic and Artificial Bee Colony algorithms are developed and proposed to achieve the above objectives. The performance of the algorithms has been enhanced with the seed solution obtained using Lagrange Multiplier method. The complex assembly problems proposed by various authors with the required parameters have been considered for investigating the proposed method. The critical dimensions of the assemblies are fixed and the nominal dimension has been varied with its tolerances. The resultant manufacturing cost by various methods is presented and compared with corresponding nominal dimensions and tolerances. Based on the percentage of improvement of manufacturing cost, it is observed that the Artificial Bee Colony algorithm outperforms.

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“…Gonzalez and Sanchez (2009) developed a methodology to allocate optimal statistical tolerances to dependent variables and the manufacturing costs were minimized using a statistical approach. Forouraghi (2009) developed a methodology to allow an automatic tolerance allocation capable of minimizing manufacturing costs based on particle swarm optimization (PSO) algorithm. Sivakumar and Stalin (2009) used a Lagrange multiplier method for selection of alternative processes for tolerance allocation and total manufacturing cost minimization.…”

Section: Introductionmentioning

confidence: 99%

Advanced optimal tolerance design of machine elements using teaching-learning-based optimization algorithm

Rao

More²

2014

Production & Manufacturing Research

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Tolerance design has become a very key issue in product and process development because of an informal compromise between functionality, quality, and manufacturing cost. The problem formulation becomes complex with simultaneous selection of design and manufacturing tolerances and the optimization problem is difficult to solve with the traditional optimization techniques. In this paper, a recently developed optimization algorithm called teaching-learning-based optimization (TLBO) is used for optimal selection of design and manufacturing tolerances with an alternative manufacturing process to obtain the optimal solution which is nearer to global optimal solution. Three problems are considered and these are: overrunning clutch assembly, knuckle joint assembly with three arms, and a helical spring. Out of these three problems, the problems of overrunning clutch assembly and knuckle joint assembly with three arms are multi-objective optimization problems and the helical spring problem is a single-objective problem. The comparison of the proposed algorithm is made with the genetic algorithm (GA), Non-dominated sorting genetic algorithm-II (NSGA-II), and multi-objective particle swarm optimization algorithm (MOPSO). It is found that the TLBO algorithm has produced better results when compared to those obtained by using GA, NSGA-II, and MOPSO algorithms.

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Optimal tolerance allocation using a multiobjective particle swarm optimizer

Cited by 31 publications

References 18 publications

Statistical geometric computation on tolerances for dimensioning

Statistical geometric computation on tolerances for dimensioning

Tolerance allocation of complex assembly with nominal dimension selection using Artificial Bee Colony algorithm

Advanced optimal tolerance design of machine elements using teaching-learning-based optimization algorithm

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