Track geometry prediction for Laser Metal Deposition based on on-line artificial vision and deep neural networks. (February 2023)

• Record Type: Journal Article
• Authors: Perani, Martina; Baraldo, Stefano; Decker, Michael; Vandone, Ambra; Valente, Anna; Paoli, Beatrice
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 79(2023)
• Publication Date: 2023-02
• Subjects: Additive manufacturing -- Artificial intelligence -- Convolutional neural networks -- Laser Metal Deposition -- Machine learning -- Process quality; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102445
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Laser Metal Deposition (LMD) is an additive manufacturing technology that attracts great interest from the industry, thanks to its potential to realize parts with complex geometries in one piece, and to repair damaged ones, while maintaining good mechanical properties. Nevertheless, the complexity of this process has limited its widespread adoption, since different part geometries, strategies and boundary conditions can yield very different results in terms of external shapes and inner flaws. Moreover, monitoring part quality during the process execution is very challenging, as direct measurements of both structural and geometrical properties are mostly impracticable. This work proposes an on-line monitoring and prediction approach for LMD that exploits coaxial melt pool images, together with process input data, to estimate the size of a track deposited by LMD. In particular, a novel deep learning architecture combines the output of a convolutional neural network (that takes melt pool images as inputs) with scalar variables (process and trajectory data). Various network architectures are evaluated, suggesting to use at least three convolutional layers. Furthermore, results imply a certain degree of invariance to the number and size of dense layers. The effectiveness of the proposed method is demonstrated basing on experiments performed on single tracks deposited by LMD using powders of Inconel 718, a relevant material for the aerospace and automotive sectors. Highlights: LMD process requires on-line control for achieving predictable results. Matrix and scalar variables are mixed in the proposed deep network structure. Geometry prediction by AI is tested on single-track experiments. Image information extracted by the CNN improves on-line track size prediction.