Assessing switch weld quality with 3D sensing and machine learning

Cosimo Patruno; Massimiliano Nitti; Angelo Cardellicchio; Nicola Mosca; Maria di Summa; Vito Renò

doi:10.1117/12.2673751

9 August 2023 Assessing switch weld quality with 3D sensing and machine learning

Cosimo Patruno, Massimiliano Nitti, Angelo Cardellicchio, Nicola Mosca, Maria di Summa, Vito Renò

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210F (2023) https://doi.org/10.1117/12.2673751
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

This paper presents a preliminary study for evaluating the quality of welds in thermomagnetic switches using 3D sensing and machine learning techniques. A 3D sensor based on laser triangulation is used to gather the point cloud of the component. The point cloud is then processed to extract hand-crafted signatures for binary classification: defective or non-defective component. Features such as Gaussian and mean curvatures, density, and quadric surface properties, are used for building these significant signatures. Different machine learning models, including decision trees, Support Vector Machines, k-nearest neighbors, random forests, ensemble classifiers, and Artificial Neural Networks, are trained using the built signatures to classify the weld as defective or non-defective. Preliminary results on actual data achieve high classification accuracy (<84%) on all the tested models.

Conference Presentation

Citation Download Citation

Cosimo Patruno, Massimiliano Nitti, Angelo Cardellicchio, Nicola Mosca, Maria di Summa, and Vito Renò "Assessing switch weld quality with 3D sensing and machine learning", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210F (9 August 2023); https://doi.org/10.1117/12.2673751

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