Tool Wear Status Recognition and Prediction Model of Milling Cutter Based on Deep Learning

Abstract

In order to ensure the reliability and stability of the manufacturing process, tool wear state should be realized real-time and accurate monitoring. This paper proposes a tool wear state recognize and predictive framework model based on Stacking Sparse De-noising Auto-Encoder (SSDAE), the Particle Swarm Optimization (PSO) and the Least Squares Support Vector Machine (LSSVM). The Stacking Sparse De-noising Auto-Encoder (SSDAE) technique is utilized to realize multi-feature signal dimension reduction with the aim of improving the prediction accuracy, which reduces the dependence on the prior knowledge of feature selection and greatly improves the modeling efficiency. PSO technique is helpful for adaptive optimization of kernel parameters, which greatly improved computing power and LSSVM model prediction accuracy. A dataset from a real machining process is utilized to verify the effectiveness of proposed model in improving the prediction accuracy. The experimental results show that a high correlation coefficient greater than 0.95 is used to extract feature vector from time domain, frequency domain and time-frequency domain three directions, and the proposed SSDAE-PSO-LSSVM model performs better than Partial Least Squares Regression (PLSR), Back Propagation Neural Network (BPNN) and Extreme Learning Machine (ELM) in terms of prediction accuracy.

Keywords

• Tool condition monitoring
• deep learning
• SSDAE
• feature fusion
• PSO-LSSVM algorithm