A machine learning model exploring creep performance of dental composites.

Journal: Dental materials : official publication of the Academy of Dental Materials
Published Date: Jul 1, 2025

Abstract

OBJECTIVES: Viscoelastic creep behaviour of RBCs determines their dimensional stability and thus contributes to their clinical performance. However, due to complex material compositional variations and differing testing protocols, comparing and analyzing the most effective factor affecting RBC viscoelastic creep behaviour is challenging. Hence, the present study aimed to establish a robust machine learning (ML) model based on datasets for creep behaviour of commercial RBCs to analyze and predict creep behaviour of RBCs and identify critical compositional factors contributing to their performance.

Authors

  • J Yang
  • Z Hao
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • D C Watts
    Dentistry, School of Medical Sciences, University of Manchester, Manchester, UK; Photon Science Institute, University of Manchester, Manchester, UK.
  • J Wang
    Joint Laboratory of Modern Agricultural Technology International Cooperation; Key Laboratory of Animal Production, Product Quality, and Security; College of Animal Science and Technology, Jilin Agricultural University, Changchun, China. moa4short@outlook.com.
  • X Jiang
    School of Computer and Control Engineering, Yantai University, Yantai 264005, China.

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