Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing

Wang, Z.G.; Wong, Yin Kwan; Rahman, Mohammad Azizur

doi:10.1007/s00170-003-1789-5

Cited by 66 publications

(23 citation statements)

References 22 publications

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“…Finally the mutation operator modifies each new solution with a low probability (mutation rate). Readers interested in the applications of GA in machining optimization are referred to [43][44][45][46].…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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Section: Optimization Algorithmsmentioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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show abstract

“…Based on literature studies [3,9,23,29,33] and some preliminary test of optimisation software, the following conditions were assumed: -population size: 200, -total number of generations: 200, -probability of crossover: 0.9, -probability of mutation: 0.01, -penalty function coefficient: 0.01, -selection method: tournament, -number of game participants: 4.…”

Section: Optimisation Of Final Segmentsmentioning

confidence: 99%

“…The problem of CNC operation optimisation in metalworking has been well explored in the literature. Many researchers concentrate on the determination of optimal cutting speed and feed rate at milling [1,21,27,30,31,33]. In the above-mentioned papers, these parameters are assumed to be constant for particular operation and part.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of CNC routing operations of wooden furniture parts

Gawronski

2012

Int J Adv Manuf Technol

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The aim of this paper is to develop mathematical model and computational procedure for optimisation of feed rate at CNC routing operations of wooden furniture parts. The proposed approach takes into consideration special characteristics of solid wood, like changes in cutting forces and obtained surface quality along with cutting direction. A multistage optimisation procedure is developed, which employs binary search method and genetic algorithm. Because of the problem of full specification of feasible region in the form of inequalities, a fuzzy logic is used for correction of intermediate results within computational procedure. The developed method is verified by optimisation of routing operation of a typical furniture part, i.e. commode top. As a result of optimisation, the processing time is reduced by 54 %.

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“…Yildiz (2009) demonstrated the superiority of the proposed hybrid method by combining immune algorithm with a hill climbing local search algorithm for solving multi-pass turning operation. Also, hybridization of simulated annealing and Hooke-Jeeves algorithm (Chen & Tsai, 1996), genetic algorithm and simulated annealing (Wang et al, 2004), Taguchi's method and genetic algorithm (Yildiz & Ozturk, 2006), etc., proved the effectiveness and efficiency of combined approach for solving machining optimization problems.…”

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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Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing

Cited by 66 publications

References 22 publications

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Optimisation of CNC routing operations of wooden furniture parts

Optimization of machining processes using pattern search algorithm

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