Deep Learning-Based Intelligent Process Monitoring of Directed Energy Deposition in Additive Manufacturing with Thermal Images
Xiang Li, Shahin Siahpour, Jay Lee, Yachao Wang, Jing Shi
Abstract
Additive manufacturing (AM) techniques have been successfully developed in the past years with the great potential of overcoming the existing obstacles in traditional manufacturing. In order to improve the quality of the manufactured parts and reduce costs, it is important to timely and accurately monitor the AM process during manufacturing. However, it remains a challenging task due to the high complexity of the AM process and the difficulty in processing the condition monitoring data. This paper proposes a deep learning-based process monitoring method for directed energy deposition in AM. The thermal images collected during manufacturing are used to identify the process condition, and a deep convolutional neural network model is proposed to build an end-to-end condition monitoring framework. Experiments on a real directed energy deposition dataset in AM are carried out for validation. The results suggest the proposed method offers a promising approach in process monitoring based on the industrial images. Furthermore, little prior knowledge on signal processing and AM is required, that largely facilitates the potential applications in the real industrial scenarios.