Metal Additive Manufacturing (AM) is a pillar of the Industry 4.0, with many attractive advantages compared to traditional subtractive fabrication technologies. However, there are many quality issues that can be an obstacle for mass production. The in-situ camera-based monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build, can be an effective solution to this problem. In this context, the use of Computer Vision and Machine Learning algorithms have a very important role. Nonetheless, they are up to this date limited by the scarcity of data for the training, as well as by the difficulty of accessing and integrating the AM process data throughout the fabrication. To tackle this problem, this paper proposes a system for in-situ monitoring that analyses images from an off-axis camera mounted on top of the machine to detect the arising defects in real-time, with automated generation of synthetic images based on Generative Adversarial Network (GAN) for dataset augmentation purposes. The computing functionalities are embedded into a holistic distributed AM platform allowing the collection, integration and storage of data at all stages of the AM pipeline.

In-situ defect detection of metal Additive Manufacturing: an integrated framework / Cannizzaro, Davide; Varrella, Antonio Giuseppe; Paradiso, Stefano; Sampieri, Roberta; Chen, Yukai; Macii, Alberto; Patti, Edoardo; Di Cataldo, Santa. - In: IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING. - ISSN 2168-6750. - 10:1(2022), pp. 74-86. [10.1109/TETC.2021.3108844]

In-situ defect detection of metal Additive Manufacturing: an integrated framework

Cannizzaro, Davide;Paradiso, Stefano;Chen, Yukai;Macii, Alberto;Patti, Edoardo;Di Cataldo, Santa
2022

Abstract

Metal Additive Manufacturing (AM) is a pillar of the Industry 4.0, with many attractive advantages compared to traditional subtractive fabrication technologies. However, there are many quality issues that can be an obstacle for mass production. The in-situ camera-based monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build, can be an effective solution to this problem. In this context, the use of Computer Vision and Machine Learning algorithms have a very important role. Nonetheless, they are up to this date limited by the scarcity of data for the training, as well as by the difficulty of accessing and integrating the AM process data throughout the fabrication. To tackle this problem, this paper proposes a system for in-situ monitoring that analyses images from an off-axis camera mounted on top of the machine to detect the arising defects in real-time, with automated generation of synthetic images based on Generative Adversarial Network (GAN) for dataset augmentation purposes. The computing functionalities are embedded into a holistic distributed AM platform allowing the collection, integration and storage of data at all stages of the AM pipeline.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2921894