|Table of Contents|

Wear loss prediction model of denture material based on radial basis function neural network

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2013年06期
Page:
922-925
Research Field:
Publishing date:

Info

Title:
Wear loss prediction model of denture material based on radial basis function neural network
Author(s):
Zheng Kan1Jia Xiuyi2Liao Wenhe1
1.School of Mechanical Engineering;
2.School of Computer Science and Engineering,NUST,Nanjing 210094,China
Keywords:
radial basis function neural network dentures wear loss TC4 alloys ten-fold cross-validation
PACS:
TH117.1
DOI:
-
Abstract:
To research the wear matching of denture materials and teeth,low speed reciprocating wear tests between teeth and TC4 alloys are performed in artificial saliva with different normal loads,sliding frequencies and cycles.Taking 11 groups of test results as training samples,a wear loss prediction model for denture material is proposed based on the radial basis function neural network(RBFNN).The mean absolute error of this model is 0.649 2 by using the 10-fold cross validation method,which verifies the correctness and rationality of the model.Dependency degree of each factor is calculated.The results show that the influences of the tooth normal load,sliding frequency and cycle on the mean absolute error are 0.626 2,0.628 8 and 0.488 6 respectively.

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Last Update: 2013-12-31