Automated Burr Quality Classification in Drilling of AA6013 Aluminum Alloy Using YOLOv10n
- 1Department of Mechatronics Engineering, Institute of Graduate Studies, Bandırma Onyedi Eylül University, Bandırma 10200, Türkiye
- 2Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandırma Onyedi Eylül University, Bandırma 10200, Türkiye
- 3Department of Naval Architecture and Marine Engineering, Maritime Faculty, Bandırma Onyedi Eylül University, Bandırma 10200, Türkiye
Abstract
Burr formation during drilling of aluminum alloys can compromise hole quality and assembly accuracy, increasing the need for secondary deburring operations. The time-consuming and operator-dependent nature of manual visual inspection creates a need for rapid and consistent automated assessment of drilling-induced burrs. This study developed a YOLOv10n-based approach for the automated classification of burr formation in drilled AA6013 aluminum alloy using optical microscope images. Hole quality was categorized into three classes: Good, Medium, and Poor. To improve the model’s generalization capability, eight datasets were generated from the original dataset using horizontal flipping (HF), 180° rotation (R180°), and combinations of these augmentation strategies. Model performance was comprehensively evaluated using Accuracy, Precision, Recall, and F1-score, together with TP, TN, FP, and FN values. The effect of the decision threshold on model sensitivity and false-negative predictions was also investigated. The best overall performance was achieved with Dataset-8, yielding an Accuracy of 0.985 and an F1-score of 0.977. Lower performance was observed for the non-augmented and individually augmented datasets, whereas combined augmentation strategies improved the model’s generalization capability. Increasing the decision threshold resulted in lower Recall and a higher number of false negatives, highlighting the importance of threshold selection in classification performance. Visual examination of the microscope images further showed that the Good, Medium, and Poor classes were associated with negligible, limited, and extensive burr formation, respectively. Integration of the trained model into a real-time imaging system enabled instantaneous burr-quality classification. As a result, the proposed approach demonstrates strong potential as a rapid, non-contact, and objective automated inspection method for post-drilling quality control.
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BibTeX
@article{kırgıl2026automated,
title = {{Automated Burr Quality Classification in Drilling of AA6013 Aluminum Alloy Using YOLOv10n}},
author = {Mikail Kırğıl and Suleyman Gokhan Taskin and Ali Ercetin},
journal = {Applied Sciences},
year = {2026},
month = oct,
doi = {10.3390/app162010012},
url = {https://doi.org/10.3390/app162010012},
note = {CC BY},
}
RIS
TY - JOUR
TI - Automated Burr Quality Classification in Drilling of AA6013 Aluminum Alloy Using YOLOv10n
AU - Mikail Kırğıl
AU - Suleyman Gokhan Taskin
AU - Ali Ercetin
PY - 2026
DA - 2026/10/26
JO - Applied Sciences
DO - 10.3390/app162010012
UR - https://doi.org/10.3390/app162010012
AB - Burr formation during drilling of aluminum alloys can compromise hole quality and assembly accuracy, increasing the need for secondary deburring operations. The time-consuming and operator-dependent nature of manual visual inspection creates a need for rapid and consistent automated assessment of drilling-induced burrs. This study developed a YOLOv10n-based approach for the automated classification of burr formation in drilled AA6013 aluminum alloy using optical microscope images. Hole quality was categorized into three classes: Good, Medium, and Poor. To improve the model’s generalization capability, eight datasets were generated from the original dataset using horizontal flipping (HF), 180° rotation (R180°), and combinations of these augmentation strategies. Model performance was comprehensively evaluated using Accuracy, Precision, Recall, and F1-score, together with TP, TN, FP, and FN values. The effect of the decision threshold on model sensitivity and false-negative predictions was also investigated. The best overall performance was achieved with Dataset-8, yielding an Accuracy of 0.985 and an F1-score of 0.977. Lower performance was observed for the non-augmented and individually augmented datasets, whereas combined augmentation strategies improved the model’s generalization capability. Increasing the decision threshold resulted in lower Recall and a higher number of false negatives, highlighting the importance of threshold selection in classification performance. Visual examination of the microscope images further showed that the Good, Medium, and Poor classes were associated with negligible, limited, and extensive burr formation, respectively. Integration of the trained model into a real-time imaging system enabled instantaneous burr-quality classification. As a result, the proposed approach demonstrates strong potential as a rapid, non-contact, and objective automated inspection method for post-drilling quality control.
SN - 2076-3417
ER -