Research of surface oxidation defects in copper alloy wire arc additive manufacturing based on time-frequency analysis and deep learning method

Abstract

In this paper a novel method was proposed for monitoring the defects of surface oxidation in copper alloy wire arc additive manufacturing (WAAM) which using voltage sensor. Due to the good fluidity of liquid copper alloy, the dimension of copper alloy WAAM process is difficult to control especially in the height direction. This will lead to error accumulation with the layers increasing during WAAM period. The phenomenon of error accumulation will cause continuous changes in the distance between the welding wire and substrate, result in inappropriate gas protection which will lead to surface oxidation defects happen. To monitoring this defect，time-frequency analysis method was used to compare the voltage signals between normal and anomaly WAAM process. The result show that the voltage signal of abnormal weld bead appears fluctuations in its peak value, and the voltage waveform of the normal WAAM process is relatively smooth. Furthermore, continuous wavelet transform (CWT) method is used to convert one-dimensional time-series voltage signals into two-dimensional time-frequency images, which is combined with deep learning method to establish an online monitoring model to monitor the surface oxidation defects in the process of copper alloy WAAM. The accuracy of the proposed model can reach 95.83%, and t-SNE(t-distributed stochastic neighbor embedding) algorithm is used to visualize the performance of the model. The result indicates that the voltage signal characteristics of normal and abnormal WAAM processes were significantly distinguished.