Optimization of the Constrained Machining Economics Problem by Geometric Programming

Ermer, D. S.

doi:10.1115/1.3428044

Cited by 124 publications

(49 citation statements)

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“…Geometric programming was earlier used for solving different machining optimization problems. Ermer (1971), Petropoulos (1973) and Lambert and Walvekar (1978) applied geometric programming for solving constrained machining economics optimization problems. Sönmez et al (1999) used geometric and dynamic programming for optimization of multi-pass slab milling and face milling for maximum production rate.…”

Section: Introductionmentioning

confidence: 99%

Optimization of machining processes using pattern search algorithm

Madić¹,

Radovanović²

2014

10.5267/j.ijiec

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Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS) algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.

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Section: Introductionmentioning

confidence: 99%

Optimization of machining processes using pattern search algorithm

Madić¹,

Radovanović²

2014

10.5267/j.ijiec

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“…Quantitative methods for optimisation of machining operations based on a single objective such as minimisation of costs and maximisation of profit or production rate have been developed. Many paradigms have been proposed for single-objective optimisation of machining operations using various techniques such as differential calculus [2], regression analysis [3], linear programming [2], geometric programming [2,4,5], stochastic programming [6] and computer simulation [7].…”

Section: Introductionmentioning

confidence: 99%

Multiple-objective optimisation of machining operations based on neural networks

Wang

1993

Int J Adv Manuf Technol

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“…Currently there is a great deal of interest in the use of Mathematical Programming techniques for the optimization of machining processes [1,3,4,7,8,9,13,14,15]. Machining is one of the more important manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Application of mathematical programming to metal cutting

Philipson

Ravindran

1979

Mathematical Programming Studies

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In this paper, a general problem involving the single-pass, single-point turning operation is introduced. Different mathematical models and solution approaches for solving various single objective problems are described. The mathematical properties of the minimization of cost and maximization of production rate solutions are discussed in detail. The solution approaches used are differential calculus, linear programming, and geometric programming. Finally, a multiple criteria machining problem is formulated and solved using goal programming techniques,

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Optimization of the Constrained Machining Economics Problem by Geometric Programming

Cited by 124 publications

References 0 publications

Optimization of machining processes using pattern search algorithm

Optimization of machining processes using pattern search algorithm

Multiple-objective optimisation of machining operations based on neural networks

Application of mathematical programming to metal cutting

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