Ball-end tool wear monitoring and multi-step forecasting with multi-modal information under variable cutting conditions

Tool condition recognition is considered an indispensable solution with significant advantages in improving production cost and quality in intelligent manufacturing. However, the emergence of complex problems such as variable cutting conditions and feature engineering further causes the technology to have a low generalization performance, which severely limits its application in engineering practice. To overcome the above problems as much as possible, a technological framework for monitoring and multi-step forecasting of ball-end tool wear based on multi-modal information under different cutting conditions is proposed. Firstly, a two-stage hybrid deep feature extraction method is proposed by monitoring the cutting vibration and power signals of the spindle. Secondly, a tool wear monitoring model based on SBiLSTM_Multihead Self-attention is proposed to adapt to different cutting conditions. On this basis, a multi-step forecasting model with CNN_SBiLSTM_Multihead Self-attention is proposed to realize the future forecasting of tool wear trend. Finally, the generalization performance of the proposed methods is investigated based on three-axis and five-axis milling experiments. The results show that the correlation coefficient of the enhanced features can reach a maximum value of 87 %. The average accuracy of the proposed monitoring model is improved by an average of 23.84 % over the conventional method. In particular, the multi-step forecasting method is more suitable for long-term forecasting under different cutting conditions. Its average accuracy reaches an average of about 0.013 in the 24-step forecasting. Therefore, the study can provide theoretical references for the application of tool condition recognition in complex machining environments in engineering practice to some extent.