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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Related Experiment Video

Updated: Aug 19, 2025

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis
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Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods.

Adam Tchórz1, Krzysztof Korona2, Izabela Krzak1

  • 1Łukasiewicz Research Network-Krakow Institute of Technology, Zakopiańska 73, 30-418 Krakow, Poland.

Materials (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

Digital image classifiers show promise for assessing ductile iron castings. This technology aids in quality control by distinguishing casting defects from nodular graphite, improving process parameter decisions.

Keywords:
3D tomography for cast metalcast irondefect analysisneural networksrecommendation system

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Area of Science:

  • Materials Science
  • Computer Science
  • Manufacturing Engineering

Background:

  • Automated defect detection in ductile iron castings is challenging due to visual similarities between voids and nodular graphite.
  • Accurate qualitative assessment of casting process parameters is crucial for producing high-quality ductile iron parts.

Purpose of the Study:

  • To evaluate the effectiveness of digital image classifiers for analyzing tomographic images of ductile iron castings.
  • To develop a decision-making support system for the qualitative assessment of casting processes.

Main Methods:

  • Development and testing of three distinct convolutional neural network (CNN) models with varying architectures.
  • Analysis of two-dimensional tomographic images using these CNN models for image classification.
  • Focus on differentiating between void images (potential defects) and nodular graphite images.

Main Results:

  • The study demonstrates the feasibility of using CNN-based image classifiers for ductile iron casting analysis.
  • Performance metrics indicate the potential for accurate classification, despite the inherent challenges.
  • The developed models form a component of a larger decision-making system for quality control.

Conclusions:

  • Digital image classification using CNNs offers a viable approach to enhance the quality assessment of ductile iron castings.
  • Further development can lead to more robust automated systems for defect detection and process optimization.
  • This research contributes to improving the efficiency and reliability of ductile iron casting production.