Evolutionary multi-objective concurrent maximisation of process tolerances

Sivakumar, K.; Balamurugan, C.; Ramabalan, S.

doi:10.1080/00207543.2010.550637

Cited by 13 publications

(10 citation statements)

References 23 publications

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“…Therefore, the verification step in this issue is a more important factor in problem modeling and it is more challenging. In the studies of optimal decisions related to manufacturing, the transformation of qualitative and quantitative attributes exists widely, such as [88][89][90][91][92][93][94][95].…”

Section: Transformation Of Qualitative and Quantitative Featuresmentioning

confidence: 99%

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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Due to its good versatility and independence, intelligent optimization algorithm has largely shortened the time of decision-making in large-scale optimization problems of manufacture. However, lower searching time often conflicts with the searching accuracy in most cases. To improve the problem solving capability, research in intelligent optimization algorithm based on different domain characteristics never stopped. From the view of manufacturing, this chapter classified and comprehensively analyzed all kinds of manufacturing optimization problems and their general methods, illustrated the application features and challenges of intelligent optimization algorithm in manufacturing, and summarized the development needs and trends of intelligent optimization algorithm in the field of manufacturing system.

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Section: Transformation Of Qualitative and Quantitative Featuresmentioning

confidence: 99%

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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“…[28], Prabhaharan et al [17], Singh et al [46], Sivakumar et al [47], and Li et al [63] showed that the GA provided a good solution to tolerance allocation and scheduling problems as compared with other optimization techniques. The ability of a GA to identify different solutions, given the same objective value, offers engineers a range of solutions from which they can then select the optimal one.…”

Section: Process Planning and Schedulingmentioning

confidence: 99%

Concurrent tolerance allocation and scheduling for complex assemblies

Geetha¹,

Ravindran²,

Kumar³

et al. 2015

Robotics and Computer-Integrated Manufacturing

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Traditionally, tolerance allocation and scheduling have been dealt with separately in the literature. The aim of tolerance allocation is to minimize the tolerance cost. When scheduling the sequence of product operations, the goal is to minimize the makespan, mean flow time, machine idle time, and machine idle time cost. Calculations of manufacturing costs derived separately using tolerance allocation and scheduling separately will not be accurate. Hence, in this work, component tolerance was allocated by minimizing both the manufacturing cost (sum of the tolerance and quality loss cost) and the machine idle time cost, considering the product sequence. A genetic algorithm (GA) was developed for allocating the tolerance of the components and determining the best product sequence of the scheduling. To illustrate the effectiveness of the proposed method, the results are compared with those obtained with existing wheel mounting assembly discussed in the literature.

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“…Sivakumar et al (2011b) used NSGA-II and MOPSO for tolerance allocation for mechanical assemblies with considering alternative manufacturing process selection. Sivakumar, Balamurugan, and Ramabalan (2012) used evolutionary NSGA-II and multiobjective differential evaluation (MODE) optimization algorithms for the simultaneous selection of optimal process tolerances for a wheel assembly. Guofu, Yuege, Ye, and Hu (2013) proposed a method of multi-objective reliability tolerance design for electronic systems.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers, except Sivakumar et al (2011aSivakumar et al ( , 2011bSivakumar et al ( , 2012 and Guofu et al (2013), considered only one objective function i.e. minimization of manufacturing cost.…”

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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Evolutionary multi-objective concurrent maximisation of process tolerances

Cited by 13 publications

References 23 publications

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Concurrent tolerance allocation and scheduling for complex assemblies

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

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