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Optimisation of shape and process parameters in metal forging using genetic algorithms

机译:使用遗传算法优化金属锻造的形状和工艺参数

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An approach to optimal design in forging is presented in this paper. The design problem is formulated as an inverse problem incorporating a finite element thermal analysis model and an optimisation technique conducted on the basis of an evolutionary strategy. A rigid viscoplastic flow-type formulation was adopted, valid for both hot and cold processes. In industrial forming processes most of the deformation energy is transformed into thermal energy. The generated heat causes the increase in temperature. External friction losses raise the temperature at the die-work-piece interface. Optimal solutions are obtained using a developed numerical algorithm based on a genetic search supported by an elitist strategy. The chosen design variables are work-piece preform shape and work-piece temperature. In order to demonstrate the efficiency of the inverse evolutionary search, specific forging cases are presented, considering the optimisation of the process parameters aiming the reduction of the difference between the realised and the prescribed final forged shape under minimal energy consumption and restricting the maximum temperature.
机译:本文提出了一种优化锻造设计的方法。设计问题被公式化为反问题,其中包含有限元热分析模型和基于演化策略进行的优化技术。采用了刚性粘塑性流动型配方,对热和冷工艺均有效。在工业成型过程中,大多数变形能都转化为热能。产生的热量导致温度升高。外部摩擦损失会提高模具接口处的温度。使用基于精英策略支持的遗传搜索的发达数值算法可获得最优解。选择的设计变量是工件预成型件的形状和工件温度。为了证明逆进化搜索的效率,提出了特定的锻造案例,其中考虑了工艺参数的优化,目的是在最小的能量消耗和限制最高温度的情况下减小实际锻造形状与规定的最终锻造形状之间的差异。

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