Scholarlink

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 s per volume supports its viability for detecting inclusions, enabling more efficient, reliable, and automated quality control.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Defect Segmentation in OCT Scans of Ceramic Parts for Non-destructive Inspection Using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Andrés Laveda-Martínez,
  • Natalia P. García-de-la-Puente,
  • Fernando García-Torres,
  • Niels Møller Israelsen,
  • Ole Bang,
  • Dominik Brouczek,
  • Niels Benson,
  • Adrián Colomer,
  • Valery Naranjo

摘要

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 s per volume supports its viability for detecting inclusions, enabling more efficient, reliable, and automated quality control.