Track geometry prediction for Laser Metal Deposition based on on-line artificial vision and deep neural networks

Perani, Martina and Baraldo, Stefano and Decker, Michael and Vandone, Ambra and Valente, Anna and Paoli, Beatrice (2023) Track geometry prediction for Laser Metal Deposition based on on-line artificial vision and deep neural networks. Robotics and Computer-Integrated Manufacturing, 79. p. 102445. ISSN 0736-5845

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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.

Item Type:	Scientific journal article, Newspaper article or Magazine article
Uncontrolled Keywords:	Additive Manufacturing, Artificial Intelligence, Convolutional Neural Networks, Laser Metal Deposition, Machine Learning, Process Quality.
Subjects:	Engineering > Production & manufacturing engineering > Manufacturing systems engineering Computer sciences > Artificial intelligence
Department/unit:	Dipartimento tecnologie innovative > Istituto sistemi e tecnologie per la produzione sostenibile
Depositing User:	Stefano Baraldo
Date Deposited:	23 Mar 2022 12:18
Last Modified:	07 Sep 2022 04:50
URI:	http://repository.supsi.ch/id/eprint/13280

Available Versions of this Item

• AI-DRIVEN PREDICTIVE MODELLING FOR LASER METAL DEPOSITION. (deposited 17 May 2021 06:37)
  • Track geometry prediction for Laser Metal Deposition based on on-line artificial vision and deep neural networks. (deposited 23 Mar 2022 12:18) [Currently Displayed]

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