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• 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. [ABSTRACT FROM AUTHOR]

• Multimodal signal features under various deviation degrees of robot trajectory demonstrate good separability. • A prediction model for robot trajectory deviation based on multimodal fusion perception is established. • The prediction model for robot trajectory deviation has strong generalization ability. During wire arc additive manufacturing (WAAM) process, the movement trajectory of robot directly determines cladding layer position, therefore, monitoring of robot trajectory is particularly important. This paper proposes an approach for monitoring robot trajectory deviation based on multimodal fusion perception. First, a visual sensing system is constructed using monochrome camera and infrared camera, which collected visual image of molten pool and measured the temperature of workpiece sidewall. Second, the weld pool contour is extracted based on deep learning, and the histogram of oriented gradient (HOG) algorithm on the basis of trilinear interpolation is adopted to extract temperature distribution characteristics of workpiece sidewall. Finally, a prediction model for robot trajectory deviation is developed using artificial neural network. The experimental results indicate that the proposed approach for monitoring robot trajectory deviation has high detection precision and strong generalization capability, it provides an effective means to ensure weldment quality in WAAM. [ABSTRACT FROM AUTHOR]