Thermal inspection and quality assessment for AFP processes via automatic defect detection and segmentation

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Zemzemoğlu, Muhammed and Ünel, Mustafa (2024) Thermal inspection and quality assessment for AFP processes via automatic defect detection and segmentation. In: 50th Annual Conference of the IEEE Industrial Electronics Society (IECON), Chicago, USA

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Abstract

Automated fiber placement (AFP) technology, while highly beneficial, is susceptible to defects that compromise the final product’s mechanical integrity. Traditional manual inspection methods are labor-intensive, error-prone, and result in significant downtime. This study introduces an innovative framework for AFP process inspection and quality assessment using thermal imaging, machine learning algorithms, and computer vision techniques. The system comprises defect detection, defect segmentation, and quality assessment components. By providing real-time feedback, it offers qualitative defect attributes (shape, size, and location) and a novel quantitative defect impact metric. An active knowledge-driven decision support system (AFP-DSS) aids operator maintenance and repair decisions. Experimental validation shows the defect detection component achieving 96.4% accuracy with a 2.8% false negative rate, and the defect segmentation component attaining 93.2% mean pixel accuracy and a mean Intersection over Union (IoU) score of 0.72. Operating in real-time, the system effectively reduces machine downtime, enhances production quality, and improves the economic viability of AFP technology.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: automated fiber placement, thermographic inspection, quality assessment, defect detection and segmentation, decision support system, computer vision, machine learning
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ163.12 Mechatronics
Divisions: Faculty of Engineering and Natural Sciences
Integrated Manufacturing Technologies Research and Application Center
Depositing User: Mustafa Ünel
Date Deposited: 29 Sep 2024 13:37
Last Modified: 29 Sep 2024 13:37
URI: https://research.sabanciuniv.edu/id/eprint/50287

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