摘要
铣刀健康状况直接影响实际生产加工过程,因此开展铣刀状态监测研究具有较大工程意义。以卷积神经网络为代表的深度学习模型已经逐渐用于监测加工过程中的刀具状态。但是这些模型的可解释性较差,预测结果的差异性也较大。作为一种新颖的卷积神经网络变种,主成分分析模型(Principal component analysis network,PCANet)的可解释性好,但是特征自监督学习能力有待提升,且相关应用案例较少。针对以上问题,拟对PCANet模型进行优化,进而提出了一种激活主成分分析-最大池化-支持向量回归(Activated PCANet with max pooling and support vector regression,APCANet-MP-SVR)模型,用于自适应提取敏感特征并准确预测刀具磨损情况。首先引入tanh激活函数,提高模型泛化能力;再采用最大池化层替代哈希编码和直方图用于特征选择,进一步降低冗余特征规模;最后建立支持向量回归模型实时预测刀具磨损值。应用案例充分证明了所提模型能够更好地用于加工现场刀具磨损值预测。
Milling tool wear condition affects real production.Thus,study on tool condition monitoring has great importance in engineering.Deep learning models,for example convolutional neural network,have been applied in tool condition monitoring during milling process.Yet the model interpretability is poor,and the prediction results vary a lot.As a novel variant of convolutional neural network,principal component analysis network(PCANet)model is well-explained.However,the self-supervised features extraction capacity still requires improvement,and few industrial cases have been studied.In order to address these problems,original PCANet model structure is optimized,then activated PCANet with max pooling and support vector regression(APCANet-MP-SVR)model is proposed to extract sensitive features in unsupervised way and predict tool wear accurately.In detail,tanh activation function is applied to improve model generalization capacity,and then max pooling layer is introduced for features selection to replace complex Hash encoding and spectrum process.In the end,support vector regression is utilized to predict current tool wear.Case has been further studied to validate the brilliant performance and industrial suitability of the proposed model.
作者
段暕
周宏娣
刘智勇
詹小斌
梁健强
史铁林
DUAN Jian;ZHOU Hongdi;LIU Zhiyong;ZHAN Xiaobin;LIANG Jianqiang;SHI Tielin(School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074;School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2023年第1期278-285,共8页
Journal of Mechanical Engineering
基金
广东省重点研发计划(2020B090927002)
国家自然科学基金(52205103,52005168)
国家重点研发计划(2020YFB1709801)资助项目。
