A novel transformer-based neural network model for tool wear estimation

This paper proposes a novel Transformer-based neural network model for accurate tool wear estimation to improve production quality and efficiency in intelligent manufacturing. The proposed method can realize indirect measurement of tool wear. Initially, the raw multi-sensor signals are processed into three kinds of temporal feature data. Next, three identical submodels are utilized to deal with the above feature data, respectively. Finally, the outputs of these three submodels are concatenated together as the input of multi-layer fully connected network for the final estimation of tool wear. Concretely, the submodels used in this work are based on the Transformer model and self-attention mechanism to capture long-term dependency. This is the first attempt to adopt Transformer and self-attention for tool wear estimation. Besides, some improvements are made in this work. For example, long short-term memory (LSTM) network is employed to enhance the ability of capturing position information. In addition, the submodel framework is applied to process the temporal feature data in parallel, which helps improve the model performance. The proposed method is demonstrated through a real-world milling dataset, including more than 900 experiments. Also, the superiority of the proposed method is verified by the comparison with other advanced methods.