Defect Segmentation in OCT scans of ceramic parts for non-destructive inspection using deep learning
This addresses quality control in ceramic manufacturing through non-destructive inspection, offering an incremental improvement with specific performance gains.
The paper tackles automatic defect detection in ceramic parts using Optical Coherence Tomography (OCT) scans by developing a U-Net-based deep learning system, achieving a Dice Score of 0.979 and an inference time of 18.98 seconds per volume.
Non-destructive testing (NDT) is essential in ceramic manufacturing to ensure the quality of components without compromising their integrity. In this context, Optical Coherence Tomography (OCT) enables high-resolution internal imaging, revealing defects such as pores, delaminations, or inclusions. This paper presents an automatic defect detection system based on Deep Learning (DL), trained on OCT images with manually segmented annotations. A neural network based on the U-Net architecture is developed, evaluating multiple experimental configurations to enhance its performance. Post-processing techniques enable both quantitative and qualitative evaluation of the predictions. The system shows an accurate behavior of 0.979 Dice Score, outperforming comparable studies. The inference time of 18.98 seconds per volume supports its viability for detecting inclusions, enabling more efficient, reliable, and automated quality control.