Determination of Optimal Machining Conditions Using Scatter Search

Chen, Mu-Chen; Chen, Kai-Ying

doi:10.1007/978-3-540-39930-8_28

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Potentially, it can be extended as an on-line quality control strategy for optimizing machining parameters based on signals from sensors. Chen & Chen (2003) have done extensive work on this technique.…”

Section: Scatter Search Technique (Ss)mentioning

confidence: 99%

Optimization of machining techniques — A retrospective and literature review

Aggarwal¹,

Singh

2005

Sadhana

112

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In this paper an attempt is made to review the literature on optimizing machining parameters in turning processes. Various conventional techniques employed for machining optimization include geometric programming, geometric plus linear programming, goal programming, sequential unconstrained minimization technique, dynamic programming etc. The latest techniques for optimization include fuzzy logic, scatter search technique, genetic algorithm, Taguchi technique and response surface methodology.

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Section: Scatter Search Technique (Ss)mentioning

confidence: 99%

Optimization of machining techniques — A retrospective and literature review

Aggarwal¹,

Singh

2005

Sadhana

112

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“…From the first studies carried out by Taylor regarding the tool's life [18,19], the set of work lines have experienced an ample growth and a wide variety of optimization techniques are used nowadays [17,19,20]: from traditional mathematic methods (linear and nonlinear programing, dynamic programing, Lagrangian multipliers or finite elements [21]) to new techniques that have been developed to solve previous limitations. These new techniques are statistical methods (like ANOVA [22], statistical regression [23], fuzzy set theory [24], Taguchi method [25], or response surface-design methodology [26,27]) and Nontraditional Algorithms; the latter consist of heuristics and metaheuristics, for example, search strategies (simulated annealing [28], Tabu search [29], or scatter search [30]) or bio-inspired algorithms, for example, artificial neural networks [31,32], naturally inspired algorithms [33], or evolutionary algorithms as genetic algorithm (GA) [34,35].…”

Section: Available Optimization Approaches In Machiningmentioning

confidence: 99%

A Review of Sustainable Machining Engineering: Optimization Process Through Triple Bottom Line

Álvarez

Bárcena

González

2016

Journal of Manufacturing Science and Engineering

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The studies about sustainable manufacturing engineering (SME) contain an increasing body of knowledge, motivated by the rising interest in the processes lifecycle sustainability. Its continuous improvement and optimization (including sustainability criteria) has become an emerging necessity. For this reason, new clean technologies and proposals of work methods are required; they have to integrate the ecological and social dimensions at an operational level in the manufacturing processes, maintaining the economic and technical feasibility attained up to this moment. However, a unified framework does not exist to orientate the lines of research in optimization when applied to sustainability. In this sense, the article reviews studies from scientific literature about sustainable machining developed in the last 15 years. The review has been carried out from the triple bottom-line (TBL) perspective, defined by the three general sustainability dimensions (economy, ecology, and equity). It contributes to the literature and current machining engineering knowledge, with its involvement in mitigating the metabolic rift. The results from the review have allowed to characterize the investigation effort, with regard to the optimization of the sustainable machining processes; even though numerous studies exist which optimize machining operations (with the aim to find the trade-off between different environmental and equity factors), in general, the technical and economic feasibilities are still the priority. The patterns defined through the analysis of the publications have established the current development trend; furthermore, as a consequence of the review results, we propose an outline of articulated lines of investigation with the aim to mitigate the metabolic rift through triple bottom-line, necessary so that machining engineering assumes the goal of finding the balance to achieve integral sustainability.

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“…Agapiou [3] has developed a multi-pass optimization system for turning processes in which the number of passes have been found by dynamic programming technique, and then the cutting conditions are optimized through the Nelder-Mead Simplex Search method by using a combined objective function. Chen and Chen [25] apply the scatter search algorithm to the determination of optimal turning conditions and present a detailed review of papers on multi-pass optimization for turning operations.…”

Section: Optimization Of Multi-pass Machining Operationsmentioning

confidence: 99%

Performance-based optimization of multi-pass face milling operations using Tribes

Onwubolu

2006

International Journal of Machine Tools and Manufacture

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Determination of Optimal Machining Conditions Using Scatter Search

Cited by 4 publications

References 29 publications

Optimization of machining techniques — A retrospective and literature review

Optimization of machining techniques — A retrospective and literature review

A Review of Sustainable Machining Engineering: Optimization Process Through Triple Bottom Line

Performance-based optimization of multi-pass face milling operations using Tribes

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