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基于BP神经网络与遗传算法的温挤压模具优化设计 被引量:7

Warm Extrusion Die Wear Optimization Design Based on BP Neural Network and Genetic Algorithm
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摘要 以汽车转向螺杆类杯-杆件的温挤压凹模为例进行模具磨损分析及其寿命预测。以影响温挤压凹模磨损的4个主要因素,即凹模入口处圆角大小、模具初始硬度、模具初始温度、摩擦因子作为工艺参数,并分别选取4个不同水平值,确定四因素四水平的32组温挤压凹模磨损试验方案,通过Deform-3D有限元数值模拟软件进行成形过程的数值模拟。以不同影响因素和对应模具的磨损量为样本训练BP神经网络,建立4个主要因素与凹模磨损量之间的映射关系,以温挤压凹模磨损量为目标函数,通过遗传算法对4个影响因素进行组合优化,使凹模磨损量最小、寿命最长。 Taking the warm extrusion concave die for connecting cup-lid of automotive steering screw as an example, the die wear and life prediction was studied. With four major factors influencing the wear and life of warm extrusion concave die,including concave die' s entrance angle, mould initial hardness, mold initial temperature and friction factor as the process parameters, and selecting four different levels respectively, 32 groups of warm extrusion concave die wear test pro- gram with four factors and four levels were determined.Numerical simulation of forming process was carried out through the Deform-3D finite element numerical simulation software.The BP neural network was trained with different influence factors and corresponding die wear as samples, and the mapping relationship between the four major factors and the die wear volumes was established.Taking warm extrusion concave die wear volume as objective function, the combinatorial optimization of four factors were carried out by genetic algorithm to minimize the wear of the die and realize the longest life of die.
出处 《润滑与密封》 CAS CSCD 北大核心 2017年第4期84-88,共5页 Lubrication Engineering
基金 国家自然科学基金项目(51257216) 吉林省教育厅项目(吉教科合字[2015]第119号)
关键词 模具磨损分析 数值模拟 BP神经网络 遗传算法 die wear analysis numerical simulation BP neural network genetic algorithm
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