期刊
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
卷 72, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2021.102183
关键词
Artificial intelligence; Nondestructive evaluation; Spot welding; Deep neural network; Autonomous prediction
资金
- US Department of Energy, Office of Nuclear Energy (Advanced Methods for Manufacturing Program)
- US Department of Energy, Office of Science (Advanced Scientific Computing Research Program)
- AI Initiative at Oak Ridge National Laboratory
The study introduces an autonomous deep-computing framework for predicting weld nugget shape and size with high accuracy. The framework successfully predicts all nugget shapes with short training time and fast inference time, suitable for various image-based weld property predictions and manufacturing processes.
The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. This study presents an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for insitu welding quality control as edge computing within an intelligent welding framework.
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