2000
DOI: 10.1016/s0890-6955(99)00063-2
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Selection of optimal conditions in multi-pass face-milling using a genetic algorithm

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Cited by 105 publications
(48 citation statements)
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“…Recently different methods have been reported in the literature to optimize machining parameters of face milling operations. These methods include Genetic Algorithm (GA) [7] using real number coding, and Genetic Algorithm (GA) using binary coding, Simulated Annealing (SA) algorithm and hybrid algorithm [8]. Some researchers optimized machining parameter for lathe operations and compared results with conventional and non-conventional methods [16].…”
Section: Introductionmentioning
confidence: 99%
“…Recently different methods have been reported in the literature to optimize machining parameters of face milling operations. These methods include Genetic Algorithm (GA) [7] using real number coding, and Genetic Algorithm (GA) using binary coding, Simulated Annealing (SA) algorithm and hybrid algorithm [8]. Some researchers optimized machining parameter for lathe operations and compared results with conventional and non-conventional methods [16].…”
Section: Introductionmentioning
confidence: 99%
“…Because the cutting conditions endlessly change, relying upon one reasoning technology to provide cutting data is very difficult. The best way to solve complex problems in the real world is to integrate these methods [23][24][25]. By combining their strength to overcome the shortcomings of a single method, a variety of mixed reasoning is produced.…”
Section: Hybrid Reasoningmentioning
confidence: 99%
“…Both of these algorithms are probabilistic search algorithms that are capable of finding globally optimal results to complicated optimisation problems. For application in machining processes, besides Dereli et al, Shunmugam et al [21] used GA to optimise the multi-pass face-milling and obtained optimal cutting parameters. Liu et al [22] improved the convergence speed of traditional GA and obtained good results by defining and changing the operating domain of GA. GA and SA have their strengths and weaknesses.…”
Section: Introductionmentioning
confidence: 99%