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Ti–6Al–4V钛合金筋板类吊挂锻造成形工艺优化及模具磨损研究 被引量:7

Optimization of Ti-6Al-4V Titanium Alloy Ribbed Plate Type Hanging Forging Forming Process and Die Wear
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摘要 目的 改善Ti–6Al–4V筋板类吊挂锻件成形缺陷,降低模具磨损。方法 通过对原始工艺中存在的折叠、充填不满等缺陷进行分析,揭示局部飞边高度对锻件充填的影响,进而优化终锻模具局部飞边高度;通过增加预锻件筋条等几何特征,对预锻模具结构进行优化设计,并分析塑性变形时筋条区域金属材料的流动情况。结合BP神经网络技术,分析锻造工艺参数对终锻模具磨损的影响,并对工艺参数组合进行优化设计。结果 基于局部飞边高度对锻件充填效果的影响规律,确定最佳局部飞边高度为4mm;通过增加预锻筋条优化预锻件结构,从而有效避免折叠缺陷;将BP神经网络技术与数值模拟结合,得到了最优工艺参数组合,有效降低了锻造模具的磨损量。结论 通过实际锻造生产试验对模拟分析结果进行了验证,固化了最佳模具结构与工艺参数组合,获得了变形均匀且无工艺缺陷的钛合金发动机吊挂锻件。 The work aims to inhibit the forming defects of Ti-6Al-4V ribbed plate type hanging forging component and reduce the die wear. The defects in conventional process such as folding and under-filling were analyzed to reveal the effects of local flash height on the forging component filling and optimize the local flash height of final forging die. The structure of the pre-forging die was optimized by adding geometrical characteristic such as bars on the pre-forging component. The flow of metal materials in bars during plastic deformation was analyzed. The effect of forging parameters on the wear of final forging die was analyzed combined with BP neutral network and the combination of forging parameters was designed through optimization.According to the effects of local flash height on the forging component filling, the ideal local flash height was determined as 4 mm. The structure of pre-forging component was optimized by adding bars, thus effectively inhibiting folding defect. Ideal forging parameters were obtained through combination of BP neural network and numerical simulation, effectively decreasing the wear volume of forging die. The simulation results are verified by actual forging production experiments. Both the ideal die structure and optimal forging parameter combination are solidified. Thus, a titanium alloy engine hanging forging component with homogenous deformation but without any forging defects is manufactured.
作者 车路长 蒋平 刘俊 熊欢 孟毅 CHE Lu-chang;JIANG Ping;LIU Jun;XIONG Huan;MENG Yi(Southwest Technology and Engineering Research Institute,Chongqing 400039,China;Chongqing University,Chongqing 400044,China)
出处 《精密成形工程》 北大核心 2022年第7期106-115,共10页 Journal of Netshape Forming Engineering
基金 国家自然科学基金面上项目(51975071) 国家工业和信息化部工业强基工程项目(TC180A3Y1/18) 重庆英才计划(CQYC202005068)。
关键词 吊挂锻件 锻造 数值模拟 工艺优化 BP神经网络 hanging forging component forging numerical simulation process optimization BP neural network
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