首页|基于改进SSA-LSTM模型的双曲度板材成形回弹预测

基于改进SSA-LSTM模型的双曲度板材成形回弹预测

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为了准确预测双曲度板材成形回弹量,控制板材加工成形质量,利用ABAQUS有限元软件对板材成形回弹过程进行仿真,构建长短时记忆网络模型(LSTM).针对麻雀搜索算法(SSA)容易陷入局部最优的问题,提出基于Circle混沌映射、反向学习、高斯与柯西变异扰动的改进麻雀搜索算法,优化LSTM模型的学习率、迭代次数、隐藏层神经元个数,并将模型与BP神经网络模型、LSTM模型和普通SSA-LSTM模型进行对比分析.结果表明,该模型对双曲度板材成形回弹预测达到整体最优预测效果,具有一定有效性和可行性.
Springback prediction of doubly curved plate forming based on improved SSA-LSTM model
In order to accurately predict the springback of doubly curved plate forming and control the forming quality of the plate,the ABAQUS finite element software was used to simulate the forming springback process of the plate,and the long-term and short-term memory network model(LSTM)was constructed.Aiming at the problem that the sparrow search algorithm(SSA)is prone to local optimum,an improved sparrow search algorithm based on circle chaotic mapping,reverse learning,gaussian and cauchy variant perturbation was proposed.The learning rate,number of iterations and number of hid-den layer neurons of the LSTM model were optimized.And the model was compared and analyzed with the BP neural net-work model,LSTM model and ordinary SSA-LSTM model.The results show that the proposed model achieves the overall optimal prediction performance on plate springback prediction,which has certain effectiveness and feasibility.

doubly curved platespringback predictionfinite elementlong short-term memory networkim-proved sparrow search algorithm

蔡一杰、刘玲、钟飞、胡勇、张云东、杨小俊

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湖北工业大学机械工程学院,湖北武汉 430068

湖北工业大学现代制造质量工程湖北省重点实验室,湖北武汉 430068

武汉理工大学船海与能源动力工程学院,湖北武汉 430063

中国海警局直属某局,海南三亚 572000

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双曲度板 回弹预测 有限元 长短时记忆网络 改进麻雀搜索算法

国家自然科学基金面上项目湖北省自然科学基金青年基金现代制造质量工程湖北省重点实验室开放基金湖北工业大学高层次人才项目

523713172022CFB882KFJJ-2021012BSQD2020010

2024

10.3404/j.issn.1672-7649.2024.16.009

舰船科学技术
中国舰船研究院,中国船舶信息中心

舰船科学技术

CSTPCD北大核心
影响因子：0.373
ISSN：1672-7649
年,卷(期)：2024.46(16)