Identification of cladding layer offset using infrared temperature measurement and deep learning for WAAM

• Published in: Optics and laser technology 2024-03, Vol.170, p.110243, Article 110243
• Main Authors: Yu, Rongwei, He, Shen, Yang, Dong, Zhang, Xiaoyong, Tan, Xiaxin, Xing, Ying, Zhang, Tianyang, Huang, Yong, Wang, Lyuyuan, Peng, Yong, Wang, Kehong
• Format: Article
• Language: English
• Subjects: Cladding layer offset; Deep learning; Infrared temperature measurement; WAAM
• ISSN: 0030-3992; 1879-2545
• DOI: 10.1016/j.optlastec.2023.110243

•The temperature distribution on weldment sidewall exhibits a certain degree of separability under different offset degrees of cladding layer.•A cladding layer offset recognition method based on infrared sensing and deep learning for WAAM is proposed.•The proposed method effectively solves the problem of arc light interference in the cladding layer offset recognition method based on visual perception. In the wire arc additive manufacturing (WAAM) procedure, the stability of the cladding layer’s formation directly impacts the final dimensional precision of the WAAM weldment. To achieve high-precision forming of the WAAM weldment, online monitoring of the cladding layer offset is essential. This article explores the monitoring technology for the cladding layer offset in the WAAM process and introduces a cladding layer offset monitoring technique using infrared temperature measurement and deep learning. First, a temperature field measurement system for the WAAM weldment is set up using an infrared thermal imager. Next, a region of interest (ROI) is selected on the weldment sidewall. The optimised histogram of oriented gradient (HOG) algorithm is then employed to extract the temperature distribution characteristics of the ROI. The separability of the temperature field on the weldment sidewall is analysed under various offset degrees of the cladding layer. Finally, using the temperature image of the ROI as input, a cladding layer offset recognition model is developed using a convolutional neural network (CNN). This study also evaluates the effect of the selected ROI position on the recognition precision of the model for the cladding layer offset. Experimental results indicate that the recognition accuracy of the model for cladding layer offset, as presented in this article, exceeds 99%. Moreover, the model demonstrates strong robustness, further enriching the intelligent monitoring methods for the WAAM process.