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CAD-CAM resin composites: Effective components for further development.

Satoshi Yamaguchi1, Hefei Li1, Takahiko Sakai2

  • 1Department of Dental Biomaterials, Osaka University Graduate School of Dentistry, 1-8 Yamadaoka, Suita, Osaka 565-0871, Japan.

Dental Materials : Official Publication of the Academy of Dental Materials
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Summary

This study identifies key components in computer-aided design and manufacturing (CAD-CAM) resin composites to enhance mechanical properties. Optimized filler size and silane ratios improve strength and longevity for dental applications.

Keywords:
Artificial intelligenceCAD-CAMFatigueFinite element analysisResin composites

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Area of Science:

  • Materials Science
  • Biomaterials Engineering
  • Computational Modeling

Background:

  • Computer-aided design and computer-aided manufacturing (CAD-CAM) resin composites are crucial in modern dentistry.
  • Optimizing their mechanical properties is essential for clinical success and longevity.
  • Understanding component interactions is key to developing advanced dental restorative materials.

Purpose of the Study:

  • To identify and summarize effective components of CAD-CAM resin composites for improved mechanical properties and further development.
  • To explore the influence of filler characteristics and matrix composition on composite performance.
  • To leverage computational methods for predicting and enhancing material behavior.

Main Methods:

  • In silico multi-scale analysis to investigate filler diameter and silane coupling ratio effects.
  • Artificial intelligence (AI) algorithms to analyze filler content and composition impacts.
  • Non-linear dynamic finite element analysis (FEA) to assess fracture behavior of CAD-CAM composite crowns.
  • Step-stress accelerating life testing (SSALT) to evaluate the longevity of CAD-CAM composite crowns.

Main Results:

  • Decreasing filler diameter increased elastic moduli and compressive strengths.
  • Reduced silane coupling ratio decreased elastic modulus and compressive strength.
  • AI identified optimal component combinations for high flexural strength (269.5 MPa).
  • FEA predicted initial crack signs in CAD-CAM composite molar crowns.
  • SSALT confirmed longevity of nanofiller-rich CAD-CAM composites with high resin matrix fractions.

Conclusions:

  • Component optimization, particularly filler size and silane ratio, significantly enhances CAD-CAM resin composite mechanical properties.
  • In silico methods, including FEA and AI, are powerful tools for predicting and improving dental material performance.
  • The integration of computational and experimental approaches accelerates the development of advanced CAD-CAM resin composites, reducing waste and saving time.