锻压技术

基于正交试验和BP神经网络的板材多点渐进成形工艺优化

英文标题：Optimization of process parameters for multi-point incremental forming of sheet metal based on orthogonal examination and BP neural network

作者：姜志宏黄信建熊洋曾艳祥

单位：江西理工大学

关键词：BP神经网络渐进成形正交试验数值模拟

分类号：TG306

出版年,卷(期):页码：2015,40(5):33-37

摘要：

针对金属板材渐进成形过程中易出现壁厚不均的问题，在多点渐进成形工艺的基础上，选定合理的工艺参数，建立有限元模型，设计正交试验方案，利用ANSYS/LS-DYNA对方锥台制件渐进成形过程进行数值模拟，并对正交试验结果进行极差分析、方差分析和BP神经网络优化。结果表明：在板材多点渐进成形中，进给量对目标制件成形区壁厚均匀性影响最大，其次是工具头半径，进给速度影响不明显；BP神经网络模型的预测结果与正交试验结果相比误差小于5%；1060铝合金板材在多点渐进成形过程中，当工具头半径为6 mm、进给量为0.25 mm、进给速度为30 mm·s-1 时，可获得壁厚较均匀的目标制件。

For the problem of uneven thickness easily caused in the incremental forming of metal sheet, based on the multi-point incremental forming process, reasonable process parameters were elected, finite element model was established, orthogonal experiment program was designed, and the incremental forming process of pyramid part was simulated numerically by ANSYS/LS-DYNA. Then, range analysis, variance analysis and BP neural network optimization of orthogonal experiment results were carried out. The results show that the feed has the greatest effect on the uniformity of wall thickness of targeted forming area in the multi-point incremental forming of sheet, and the second greatest effect is on the tool-head radius, while the impact of feed speed is little. Comparing with the results of the orthogonal experiment, the error of prediction results by BP neural network is less than 5%. Therefore, a targeted part with even wall thickness can be obtained with the tool-head radius of 6 mm, feed of 0.25 mm and feed speed of 30 mm·s-1 in the multi-point incremental forming of 1060 Al alloy sheet.

基金项目：

国家自然科学基金资助项目（50975131）

姜志宏（1977-），男，硕士，副教授黄信建（1988-），男，硕士研究生

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