Automatic quality control of aluminium parts welds based on 3D data and artificial intelligence

Angelo Cardellicchio, Massimiliano Nitti, Cosimo Patruno, Nicola Mosca, Maria Summa, Ettore Stella and Vito Renò ()
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Angelo Cardellicchio: National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
Massimiliano Nitti: National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
Cosimo Patruno: National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
Nicola Mosca: National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
Maria Summa: National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
Ettore Stella: National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
Vito Renò: National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 11, 1629-1648

Abstract: Abstract Detecting defects in welds used in critical or non-critical industrial applications is of intense interest. Several non-destructive inspection methods are available, each allowing the preservation of the integrity of the sample under analysis. However, visual-based inspection methods are the most well-assessed, which usually require human experts to inspect each sample, looking for shallow defects. This process often requires time and effort by the human operator, therefore not allowing to perform real-time defect identification, which may result in unexpected (and undesired) production costs. In recent years, several methods have been proposed to automatically deal with visual-based inspection, mainly through convolutional neural networks. However, while effective, these models require a lot of data and computational power to be trained, which is also time-consuming. This paper proposes a high-throughput data gathering and processing method using laser profilometry, along with an automatic defect detection method based on lightweight machine learning algorithms. Six different machine and deep learning approaches are compared, including SVMs, decision forests, and neural networks, achieving a top-1 accuracy of $$99.79\%$$ 99.79 % for defect identification and $$99.71\%$$ 99.71 % for defect categorization. Thanks to its effectiveness and the high data throughput achievable by data gathering, the whole method can be implemented in real production lines to minimize costs and perform real-time monitoring and defects assessment.

Keywords: 3D laser profilometry; Automatic quality control of aluminum weldings; Machine learning; Deep learning (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-023-02124-1

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