An intelligent prediction method of surface residual stresses based on multi-source heterogeneous data

Surface residual stresses (Rs) have a significant impact on the performance of machined parts, including fatigue life and corrosion resistance. To enable online monitoring of Rs, many studies have focused on obtaining real-time Rs. However, direct measurement methods, including destructive and non-destructive techniques, will consume too much time or even damage the machined surface. Meanwhile, prediction methods rarely consider dynamic factors as identifying key features from dynamic data is challenging for humans. Therefore, this paper proposes an intelligent prediction method of Rs based on multi-source heterogeneous data, which contain cutting force, cutting temperature, power consumption, and cutting noise. Firstly, an Improved Convolutional Neural Network is established to identify features from the dynamic heterogeneous data. The mean training identification accuracy reaches 99.6%, which is significantly better than that (71%) obtained by the original convolutional neural network. Then, the Principal Component Analysis is built to automatically determine the key features, which benefit the subsequent Rs prediction. Finally, based on the key features, the Gaussian Process Regression is proposed to predict Rs in two directions. From the various experiments, the performance of the intelligent prediction method is validated, and the prediction accuracy rates for two directions reach 99.10% and 99.13%, respectively. Based on the proposed method, the real-time Rs can be predicted with the key features, which are automatically extracted from the multi-source heterogeneous data. This provides the basis for surface quality monitoring based on online data and greatly improves the level of intelligent manufacturing.