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作 者:周梦洁 尹玲[2,3] 张丽娟[3] 张斐 宋加雷 叶正伟 ZHOU Mengjie;YIN Ling;ZHANG Lijuan;ZHANG Fei;SONG Jialei;YE Zhengwei(School of Mechanical Engineering,Dongguan University of Technology,Dongguan Guangdong 523808,China;Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies of Urban Lifeline Engineering,Dongguan University of Technology,Dongguan Guangdong 523808,China;DGUT-CNAM Institute,Dongguan University of Technology,Dongguan Guangdong 523106,China;Institute of Science and Technology Innovation,Dongguan University of Technology,Dongguan Guangdong 523808,China)
机构地区:[1]东莞理工学院机械工程学院,广东东莞523808 [2]东莞理工学院,广东省城市生命线工程智慧防灾与应急技术重点实验室,广东东莞523808 [3]东莞理工学院法国国立工艺学院联合学院,广东东莞523106 [4]东莞理工学院科技创新研究院,广东东莞523808
出 处:《机床与液压》2024年第23期136-142,共7页Machine Tool & Hydraulics
基 金:广东省城市生命线工程智慧防灾与应急技术重点实验室(2022B1212010016);东莞市科技特派员项目(20231800500082,20231800500632)。
摘 要:针对传统单一温度测点在监测数控机床主轴温度变化方面的局限性,以及基于反向传播神经网络(BP)的热误差模型在精度、收敛性及鲁棒性上存在的不足,提出一种基于多温度传感器的自适应粒子群优化反向传播神经网络(IAPSO-BP)模型,旨在提高主轴热误差的辨识精度。引入多个温度传感器,以全面监测主轴的温度信息。自适应粒子群算法的应用减少了人工参数调整的需求,并提高了模型的泛化能力。以特定型号的机床为例,通过实切加工实验建立主轴热误差模型,并对其有效性及鲁棒性进行验证。结果表明:与传统BP神经网络预测模型相比,所提IAPSO-BP模型的均方误差降低67.45%,最大绝对残差减少69.62%,拟合优度提升4.29%,证明了模型的优越性。In view of the limitations of the traditional single temperature measuring point in monitoring the temperature change of the spindle of CNC machine tools,and the shortcomings of the thermal error model based on backpropagation neural network(BP)in accuracy,convergence and robustness,an improved adaptive particle swarm optimization backpropagation neural network(IAPSO-BP)model based on multiple temperature sensors was proposed,aiming to improve the identification accuracy of thermal error of spindle.Multiple temperature sensors were introduced to comprehensively monitor the temperature information of the spindle.The application of adaptive particle swarm optimization could reduce the need of manual parameter adjustment and improve the generalization ability of the model.Taking a specific type of machine tool as an example,the thermal error model of the spindle was established through the real cutting experiment,and its validity and robustness were verified.The experimental results show that compared with the traditional BP neural network prediction model,the mean square error of the proposed IAPSO-BP model is reduced by 67.45%,the maximum absolute residual is reduced by 69.62%,and the goodness of fit is increased by 4.29%,which proves the superiority of the model.
分 类 号:TH161[机械工程—机械制造及自动化]
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