首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Machining parameters optimisation for turning cylindrical stock into a continuous finished profile using genetic algorithm (GA) and simulated annealing (SA)
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Machining parameters optimisation for turning cylindrical stock into a continuous finished profile using genetic algorithm (GA) and simulated annealing (SA)

机译:使用遗传算法(GA)和模拟退火(SA)优化将圆柱形坯料转变为连续成品轮廓的加工参数

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摘要

The economics of machining have been of interest to many researchers. Many researchers have dealt with the optimisation of machining parameters for turning operations with constant diameters only. All CNC machines produce finished components from bar stock. Finished profiles consist of straight turning, facing, taper and circular machining. This research concentrates on optimising the machining parameters for turning cylindrical stock into continuous finished profiles. Arriving at a finished profile from a cylindrical stock is done in two stages, rough machining and finish machining. Rough machining consists of multiple passes and finish machining consists of single-pass contouring after the stock is removed in rough machining. The machining parameters in multipass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost. In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost, subject to a set of practical constraints. The constraints considered in this problem are cutting force, power constraint and tool tip temperature. Due to high complexity of this machining optimisation problem, a simulated annealing (SA) and genetic algorithm (GA) are applied to resolve the problem. The results obtained from GA and SA are compared.
机译:机械加工的经济性已引起许多研究人员的兴趣。许多研究人员只针对直径恒定的车削加工进行了加工参数的优化。所有的CNC机器均由棒材生产成品零件。精加工的型材包括直车削,端面,锥度和圆形加工。这项研究集中在优化将圆柱形坯料转变为连续成品轮廓的加工参数上。从圆柱形坯料到达精加工轮廓的过程分为两个阶段,即粗加工和精加工。粗加工中去除毛坯后,粗加工包括多道次,精加工包括单道轮廓。多道次车削中的加工参数为切削深度,切削速度和进给。加工性能由生产成本衡量。在本文中,针对连续型材加工的最佳加工参数是根据最低生产成本确定的,但要遵守一系列实际约束。在此问题中考虑的约束是切削力,功率约束和刀尖温度。由于此加工优化问题的复杂性很高,因此采用了模拟退火(SA)和遗传算法(GA)来解决该问题。比较了从GA和SA获得的结果。

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