AWANDT: assessing welding anomalies via non-destructive tests

Adriano Liso; Angelo Cardellicchio; Cosimo Patruno; Massimiliano Nitti; Ettore Stella; Vito Renò

doi:10.1117/12.2673651

9 August 2023 AWANDT: assessing welding anomalies via non-destructive tests

Adriano Liso, Angelo Cardellicchio, Cosimo Patruno, Massimiliano Nitti, Ettore Stella, Vito Renò

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 1262109 (2023) https://doi.org/10.1117/12.2673651
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

Detecting defects that may arise in weldings used in either critical or non-critical industrial applications is an extensive area of active research. As such, non-destructive tests are often required, mainly to preserve the integrity of normal samples while discarding defective ones. Amongst these types of tests, the most relevant are visual inspections which, however, require a considerable effort by a domain expert. Still, as these tasks require relevant efforts and could be biased by subjectivity and inexperience, the development of automated, objective tools that provide early warnings about the occurrence of one or more anomalies has become essential. To deal with these issues, this work proposes a framework for detecting anomalies in linear aluminum welding. Specifically, the framework starts by acquiring the three-dimensional representation of the welding using a 3D laser profiler. Afterward, a semi-supervised approach is followed by training a deep autoencoder on non-defective data samples, allowing the model to learn a mapping function that successfully reconstructs non-defective weldings with a small reconstruction error while providing a large error for abnormal data samples. Hence, by applying a proper threshold on the reconstruction error, which can be estimated via statistical analysis, the framework can provide real-time early warnings concerning surface defects on the welding. Different structures of autoencoder have been tested and found to reach an F₁ score of over 90%.

Conference Presentation

Citation Download Citation

Adriano Liso, Angelo Cardellicchio, Cosimo Patruno, Massimiliano Nitti, Ettore Stella, and Vito Renò "AWANDT: assessing welding anomalies via non-destructive tests", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 1262109 (9 August 2023); https://doi.org/10.1117/12.2673651

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available