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Related Experiment Video

Updated: Jun 26, 2026

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography
06:53

Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography

Published on: January 25, 2019

CeraMIRScan: Mid-infrared OCT Scan Dataset for Ceramic Quality Assessment.

Natalia P García-de-la-Puente1, Fernando García-Torres2, Andrés Laveda-Martínez3

  • 1Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain. napegar@upv.es.

Scientific Data
|June 24, 2026
PubMed
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This summary is machine-generated.

A new dataset, CeraMIRScan, aids ceramic quality assessment using Mid-infrared Optical Coherence Tomography (MIR-OCT) and deep learning. It enables automated defect detection and segmentation in industrial applications.

Area of Science:

  • Materials Science
  • Non-Destructive Testing (NDT)
  • Artificial Intelligence

Background:

  • Mid-infrared Optical Coherence Tomography (MIR-OCT) offers high-resolution imaging for industrial NDT.
  • Deep Learning (DL) models for defect detection in MIR-OCT scans, especially for ceramics, are underdeveloped.
  • Automated quality assessment of 3D printed ceramics using MIR-OCT is an emerging area.

Purpose of the Study:

  • Introduce the CeraMIRScan dataset for ceramic quality assessment.
  • Provide a benchmark for developing DL models for defect detection and segmentation in MIR-OCT scans.
  • Facilitate advancements in automated quality control for ceramic manufacturing.

Main Methods:

  • Developed the CeraMIRScan dataset comprising 29 MIR-OCT volumes (21,882 B-scans) of 3D printed ceramics.

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Last Updated: Jun 26, 2026

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  • Expert-annotated binary masks identify defects like pores, delaminations, and inclusions.
  • Implemented a U-Net architecture for baseline DL-based defect segmentation.

    • Main Results:

    • The CeraMIRScan dataset contains 41.38% of images with visible anomalies.
    • Baseline U-Net model achieved 80.55% precision, 80.00% recall, and 80.27% Dice score for defect segmentation.
    • Demonstrated the dataset's utility for training DL models for ceramic defect analysis.

    Conclusions:

    • CeraMIRScan serves as a valuable resource for advancing automated quality assessment in ceramics.
    • The dataset supports the development of MIR-OCT-based defect characterization techniques.
    • Highlights the potential of DL for enhancing NDT in industrial ceramic applications.