Inline-Acquired Product Point Clouds for Non-Destructive Testing: A Case Study of a Steel Part Manufacturer

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Udgivet i:Machines vol. 13, no. 2 (2025), p. 88
Hovedforfatter: Ntoulmperis, Michalis
Andre forfattere: Discepolo, Silvia, Castellini, Paolo, Catti, Paolo, Nikolakis, Nikolaos, van de Kamp, Wilhelm, Alexopoulos, Kosmas
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MDPI AG
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024 7 |a 10.3390/machines13020088  |2 doi 
035 |a 3171132320 
045 2 |b d20250101  |b d20251231 
084 |a 231531  |2 nlm 
100 1 |a Ntoulmperis, Michalis  |u Laboratory for Manufacturing Systems & Automation (LMS), Department of Mechanical Engineering & Aeronautics, University of Patras, Rio, 26504 Patras, Greece 
245 1 |a Inline-Acquired Product Point Clouds for Non-Destructive Testing: A Case Study of a Steel Part Manufacturer 
260 |b MDPI AG  |c 2025 
513 |a Journal Article Case Study 
520 3 |a Modern vision-based inspection systems are inherently limited by their two-dimensional nature, particularly when inspecting complex product geometries. These systems are often unable to capture critical depth information, leading to challenges in accurately measuring features such as holes, edges, and surfaces with irregular curvature. To address these shortcomings, this study introduces an approach that leverages computer-aided design-oriented three-dimensional point clouds, captured via a laser line triangulation sensor mounted onto a motorized linear guide. This setup facilitates precise surface scanning, extracting complex geometrical features, which are subsequently processed through an AI-based analytical component. Dimensional properties, such as radii and inter-feature distances, are computed using a combination of K-nearest neighbors and least-squares circle fitting algorithms. This approach is validated in the context of steel part manufacturing, where traditional 2D vision-based systems often struggle due to the material’s reflectivity and complex geometries. This system achieves an average accuracy of 95.78% across three different product types, demonstrating robustness and adaptability to varying geometrical configurations. An uncertainty analysis confirms that the measurement deviations remain within acceptable limits, supporting the system’s potential for improving quality control in industrial environments. Thus, the proposed approach may offer a reliable, non-destructive inline testing solution, with the potential to enhance manufacturing efficiency. 
653 |a Nondestructive testing 
653 |a Inspection 
653 |a Quality control 
653 |a Artificial intelligence 
653 |a Scanners 
653 |a Lasers 
653 |a Sensors 
653 |a Triangulation 
653 |a Robots 
653 |a Algorithms 
653 |a Manufacturers 
653 |a Methods 
653 |a Computer aided design--CAD 
653 |a Manufacturing 
653 |a Uncertainty analysis 
653 |a Dimensional analysis 
653 |a Vision systems 
653 |a Case studies 
653 |a Steel products 
700 1 |a Discepolo, Silvia  |u Dip. di Ingegneria Industrial e Scienze Matematiche, Universita Politecnica delle Marche, Via Brecce Bianche 10, 60131 Ancona, Italy 
700 1 |a Castellini, Paolo  |u Dip. di Ingegneria Industrial e Scienze Matematiche, Universita Politecnica delle Marche, Via Brecce Bianche 10, 60131 Ancona, Italy 
700 1 |a Catti, Paolo  |u Laboratory for Manufacturing Systems & Automation (LMS), Department of Mechanical Engineering & Aeronautics, University of Patras, Rio, 26504 Patras, Greece 
700 1 |a Nikolakis, Nikolaos  |u Laboratory for Manufacturing Systems & Automation (LMS), Department of Mechanical Engineering & Aeronautics, University of Patras, Rio, 26504 Patras, Greece 
700 1 |a van de Kamp, Wilhelm  |u VDL WEWELER bv, 7325 WC Apeldoorn, The Netherlands 
700 1 |a Alexopoulos, Kosmas  |u Laboratory for Manufacturing Systems & Automation (LMS), Department of Mechanical Engineering & Aeronautics, University of Patras, Rio, 26504 Patras, Greece 
773 0 |t Machines  |g vol. 13, no. 2 (2025), p. 88 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3171132320/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3171132320/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3171132320/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch