Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing

Alessandro Niccolai, Davide Caputo, Leonardo Chieco, Francesco Grimaccia and Marco Mussetta
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Alessandro Niccolai: Politecnico di Milano, Dipartimento di Energia, via La Masa 34, 20156 Milano, Italy
Davide Caputo: GKN Aerospace Norway AS, Kirkegardsveien 45 N, 3616 Kongsberg, Norway
Leonardo Chieco: MASMEC S.p.A., via dei Gigli 21, 70026 Modugno, Italy
Francesco Grimaccia: Politecnico di Milano, Dipartimento di Energia, via La Masa 34, 20156 Milano, Italy
Marco Mussetta: Politecnico di Milano, Dipartimento di Energia, via La Masa 34, 20156 Milano, Italy

Mathematics, 2021, vol. 9, issue 11, 1-16

Abstract: Fluorescent penetrant inspection (FPI) is a well-assessed non-destructive test method used in manufacturing for detecting cracks and other flaws of the product under test. This is a critical phase in the mechanical and aerospace industrial sector. The purpose of this work was to present the implementation of an automated inspection system, developing a vision-based expert system to automate the inspection phase of the FPI process in an aerospace manufacturing line. The aim of this process was to identify the defectiveness status of some mechanical parts by the means of images. This paper will present, test and compare different machine learning architectures to perform the automated defect detection on a given dataset. For each test sample, several images at different angles were captured to properly populate the input dataset. In this way, the defectiveness status should be found combining the information contained in all the pictures. In particular, the system was designed for increasing the reliability of the evaluations performed on the airplane part, by implementing proper artificial intelligence (AI) techniques to reduce current human operators’ effort. The results show that, for applications in which the dataset available is quite small, a well-designed feature extraction process before the machine learning classifier is a very important step for achieving high classification accuracy.

Keywords: image processing; non-destructive testing; artificial neural networks; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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