A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring

With the development of Industry 4.0 technology including the Internet of Things (IoT) and deep learning techniques, it is important to reduce maintenance costs and ensure the safety of manufacturing process. The cutting tool degradation can cause significant economic losses and risks for machine users. Accurate prediction of cutting tool is important for making full use of cutter life. Deep learning plays an important role for tool condition monitoring. To overcome these difficulties, this paper aims to propose a new approach in the application of deep learning to estimate the tool wear during the milling process. The proposed methodology is based on the data-driven approach using Variational Mode Decomposition (VMD) and deep learning. Two deep learning machines used in this study, Convolutional Neural Networks (CNN) and Bidirectional long short-term memory (BiLSTM) to achieve collaborative data mining on (VMD) and to enhance the accuracy of modeling. VMD is a new decomposition technique used to decompose signal into sub-time series called intrinsic mode functions (IMFs). However, the VMD performances specifically depend on the constraints parameters that need to be pre-determined for VMD method especially the number of modes. The model development based on 1D-CNN and BiLSTM are selected by using the IMFs as inputs. The performance of the proposed approach is further improved by using the combined CNN and BiLSTM and has shown higher performances in prediction, compared to traditional learning techniques and adopted in previous works highlight the proposed prognostics method's superiority. Among all models, the VMD-CNN-BiLSTM achieve the best performance of modeling with respect to efficiency and effectiveness.