[1]郑 侃,贾修一,廖文和.基于径向基神经网络的义齿材料磨损量预测模型[J].南京理工大学学报(自然科学版),2013,37(06):922-925.
 Zheng Kan,Jia Xiuyi,Liao Wenhe.Wear loss prediction model of denture material based on radial basis function neural network[J].Journal of Nanjing University of Science and Technology,2013,37(06):922-925.
点击复制

基于径向基神经网络的义齿材料磨损量预测模型
分享到:

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

卷:
37卷
期数:
2013年06期
页码:
922-925
栏目:
出版日期:
2013-12-31

文章信息/Info

Title:
Wear loss prediction model of denture material based on radial basis function neural network
作者:
郑 侃1贾修一2廖文和1
南京理工大学 1.机械工程学院;
2.计算机科学与工程学院,江苏 南京 210094
Author(s):
Zheng Kan1Jia Xiuyi2Liao Wenhe1
1.School of Mechanical Engineering;
2.School of Computer Science and Engineering,NUST,Nanjing 210094,China
关键词:
径向基神经网络 义齿 磨损量 TC4钛合金 十折交叉验证法
Keywords:
radial basis function neural network dentures wear loss TC4 alloys ten-fold cross-validation
分类号:
TH117.1
摘要:
为研究义齿材料与天然牙之间的磨损匹配性,在人工唾液润滑的条件下,采用天然牙与TC4钛合金在不同法向载荷、频率以及循环次数的工况下进行低速往复磨损实验。以11组实验结果为训练样本,基于径向基神经网络,提出了一种可预测义齿材料磨损量的模型。采用十折交叉验证法得到模型的绝对平均误差为0.649 2,验证了该模型的准确性和合理性。通过计算各因素的依赖度得到,牙齿摩擦的法向载荷、频率、循环次数对该模型的绝对平均误差影响分别为0.626 2、0.628 8和0.488 6。
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.

参考文献/References:

[1] 于世宾,王美青,赵守亮.牙齿磨损的病因学研究进展[J].牙体牙髓牙周病学杂志,2003,13(12):707-710.
Yu Shibin,Wang Meiqing,Zhao Shouliang.Research progression on the etiology of tooth wear[J].Chinese Journal of Conservative Dentistry,2003,13(12):707-710.
[2]Zou Lifong,Cherukara G,Hao Pengwei,et al.Geometrics of tooth wear[J].Wear,2009,266(5/6):605-608.
[3]Pasaribu H R,Reuver K M.Environmental effects on friction and wear of dry sliding zirconia and alumina ceramics doped with copper oxide[J].International Journal of Refractory Metals and Hard Materials,2009,23(4/6):386-390.
[4]Davim J P,Santos E.Comparative study of friction behaviour of alumina and zirconia ceramics against steel under water lubricated conditions[J].Industrial Lubrication and Tribology,2008,60(4):178-182.
[5]Ghazal M,Yang B,Ludwig K,et al.Two-body wear of resin and ceramic denture teeth in comparison to human enamel[J].Dental Materials,2008,24(4):502-507.
[6]郑靖.牙齿的摩擦学特性研究[D].成都:西南交通大学机械工程学院,2004:1-107.
[7]Yu J b.Online tool wear prediction in drilling operations using selective artificial neural network ensemble model[J].Neural Computing and Applications,2011,20(4):473-485.
[8]王文健,陈明韬,刘启跃.基于BP神经网络的钢轨磨损量预测[J].润滑与密封,2007,32(12):20-22.
Wang Wenjian,Chen Mingtao,Liu Qiyue.Prediction of wear volumes of rail steel based on BP neural network[J].Lubrication Engineering,2007,32(12):20-22.
[9]宋江腾,曾攀,赵加清,等.基于RBF神经网络模型的司太立合金磨损量预测[J].润滑与密封,2011,36(3):30-32,64.
Song Jiangteng,Zeng Pan,Zhao Jiaqing,et al.Analysis of stellite alloys wearing prediction based on radial basis function neural network[J].Lubrication Engineering,2011,36(3):30-32,64.
[10]Stober T,Lutz T,Gilde H,et al.Wear of resin denture teeth by two-body contact[J].Dental Materials,2006,22(3):243-249.
[11]Li H,Zhou Z R.Wear behavior of human teeth in dry and artificial saliva conditions[J].Wear,2002,249(10/11):980-984.
[12]林棻,赵又群.汽车侧偏角估计方法比较[J].南京理工大学学报,2009,33(1):122-126,131.
Lin Fen,Zhao Youqun.Comparison of methods for estimating vehicle side slip angle[J].Journal of Nanjing University of Science and Technology,2009,33(1):122-126,131.
[13]隆金玲.Sum-of-Product神经网络和径向基函数神经网络的逼近能力研究[D].大连:大连理工大学数学科学学院,2008:8-12.

备注/Memo

备注/Memo:
收稿日期:2012-08-06 修回日期:2012-10-15
基金项目:中央高校基本科研业务费专项资金(2012XQTR001); 江苏省自然科学基金(BK2012402)
作者简介:郑侃(1983-),男,博士,讲师,主要研究方向:口腔修复体先进制造技术,E-mail:zhengkan@njust.edu.cn。
引文格式:郑侃,贾修一,廖文和.基于径向基神经网络的义齿材料磨损量预测模型[J].南京理工大学学报,2013,37(6):922-925.
投稿网址:http://njlgdxxb.paperonce.org
更新日期/Last Update: 2013-12-31