Prediction of tool wear value based on meta-learning and multi-head attention
To achieve effective transfer of tool wear prediction values based on neural network methods under multiple working conditions,a method combining Model Agnostic Meta-Learning(MAML)and Multi-Head Attention(MHA)was proposed to realize tool wear prediction under multiple working conditions.The multi-dimensional in-formation in the cutting process of the tool was collected by multiple sensors,and the time-frequency feature matrix was constructed by extracting time-domain,frequency-domain and time-frequency domain features.The MHA model was utilized as the base model,and the time-frequency feature samples constructed from historical working condition information were leveraged to train this model via the MAML approach to acquire the optimal initialization parameters for the MHA model.In the new working condition,the optimal initialization MHA model was iteratively trained with a small number of initial wear samples several times to adapt to the new condition,thereby predicting the tool wear value for the new working conditions.Finally,relevant experiments demonstrated that the proposed method could achieve effective model transfer in tool wear prediction under multiple working conditions.
万方数据
维普
