In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion

Abstract

Online process monitoring and quality control has been a long-standing challenge for variable polarity plasma arc welding (VPPAW) due to the inherent instability and fluctuation of the keyhole molten pool. This work developed an innovative welder intelligence-enhanced deep random forest fusion (WI-DRFF) approach, aiming to describe the dynamics of front-side molten pool and accurately predict the weld penetration. Based on the human welder’s prior knowledge, we firstly proposed an image processing algorithm to extract the low-level handcrafted features, which could quantitatively describe the geometrical appearance of the keyhole. Afterwards, we constructed a convolutional neural network (CNN) to learn the high-level discriminative features of weld pool and interpret the physical characteristics of the deep features with visualization. Finally, we incorporated the handcrafted keyhole features and deep features to concatenate a multi-level feature vector for predicting the weld penetration based on random forest (RF) classifier. Extensive experiments demonstrate that our proposed approach yields a remarkable classification performance comparing with state-of-the-art machine learning algorithms even with limited training data. This approach is a new paradigm in the digitization and intelligence of welding process and can be exploited to provide a feedback in an adaptive quality control system.