摘要
为了提高加工过程中刀具磨损监测精度,提出一种基于改进的鲸鱼优化算法(IWOA)和改进的高效通道注意力机制(IECA)的双向长短期记忆网络(BiLSTM)模型。通过对PHM2010刀具磨损数据进行片段截取并提取多域特征,再结合皮尔逊系数筛选得到刀具磨损强相关特征。输入特征训练模型,模型中BiLSTM模块能有效捕捉数据中的时序特征;IECA注意力机制模块能提高特征表征能力;IWOA模块能优化模型超参数,进一步提高模型精度。最后基于三折交叉验证测试模型性能,并与其他多个模型进行对比,结果表明,IWOA-IECA-BiLSTM刀具磨损监测模型在多数测试集上具有最佳表现,在C_(1)、C_(4)、C_(6)三个测试集上均方根误差分别低至6.5、12.46、9.28。
To improve the monitoring accuracy of tool wear during machining,a BiLSTM model based on IWOA and IECA mechanism was proposed.Tool wear data segments from the PHM2010 dataset were intercepted,and multi-domain features were extracted.Tool wear strongly correlated features were then obtained by screening with the Pearson correlation coefficient.The input features were used to train the model.The BiLSTM module in the model effectively captured temporal features within the data.The IECA attention mechanism module enhances the feature representational capability.The IWOA module optimized the model's hyperparameters,further improving the model accuracy.The model performance was finally tested based on three-fold cross-validation and compared with several other models.The results demonstrate that the IWOA-IECA-BiLSTM tool wear monitoring model achieves the best performance on most test sets.On test sets C_(1),C_(4) and C_(6),the root mean square error(RMSE)values are as low as 6.5,12.46,and 9.28,respectively.
作者
包振科
曹华军
秦逢泽
陈志祥
陶桂宝
BAO Zhenke;CAO Huajun;QIN Fengze;CHEN Zhixiang;TAO Guibao(State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing,400044)
出处
《中国机械工程》
北大核心
2025年第12期2936-2943,共8页
China Mechanical Engineering
基金
国家重点研发计划(2022YFB3206700)。
关键词
刀具磨损
改进鲸鱼优化算法
改进高效通道注意力机制
双向长短期记忆网络
tool wear
improved whale optimization algorithm(IWOA)
improved efficient channel attention(IECA)mechanism
bi-directional long short-term memory(BiLSTM)network
