An evaluation of the capabilities of image-based metal component defect recognition with deep learning techniques
Michał P. Wójcik
, Kacper Pawlikowski, Łukasz Madej
AGH University of Krakow, Mickiewicza 30, 30-059 Krakow, Poland.
DOI:
https://doi.org/10.7494/cmms.2024.3.0839
Abstract:
In the era of Industry 4.0, deploying highly specialised machine learning models trained on unique and often scarce datasets is an attractive solution for advancing automated quality control and minimising production costs. Therefore, the main aim of this research is to evaluate the capabilities of three deep learning models (ResNet-18, ResNet-50 and SE-ResNeXt-101 (32 × 4d)) in the identification of surface defects in forged products. Leveraging advanced photography techniques, including studio lighting and a shadowless box, high-quality images of complex product surfaces were acquired for the training data set. Given the relatively small size of the image dataset, aggressive data augmentation techniques were introduced during the training and evaluation process to ensure robust model generalisation ability. The results obtained demonstrate the significant impact of data augmentation on model performance, highlighting its importance in training and evaluating deep learning models with limited data. This research also emphasises the need for innovative data pre-processing strategies in an efficient and robust machine learning model delivery to the industrial environment.
Cite as:
Wójcik, M.P., Pawlikowski, K. & Madej, Ł. (2024). An evaluation of the capabilities of image-based metal component defect recognition with deep learning techniques. Computer Methods in Materials Science, 24(3), 33-40. https://doi.org/10.7494/cmms.2024.3.0839
Article (PDF):
Accepted Manuscript – final pdf version coming soon
Keywords:
Deep learning, Convolutional neural networks, Image classification, Data augmentation, Quality control, Surface defect recognition, Forging
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