1. In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion
- Author
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Zhishui Yu, Di Wu, Yiming Huang, Minghua Hu, and Peilei Zhang
- Subjects
0209 industrial biotechnology ,Materials science ,business.industry ,Strategy and Management ,Feature vector ,Image processing ,Pattern recognition ,02 engineering and technology ,Management Science and Operations Research ,021001 nanoscience & nanotechnology ,Convolutional neural network ,Industrial and Manufacturing Engineering ,Random forest ,Visualization ,Plasma arc welding ,020901 industrial engineering & automation ,Weld pool ,Artificial intelligence ,0210 nano-technology ,business ,Keyhole - 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.
- Published
- 2021