• CN：11-2187/TH
  • ISSN：0577-6686
机械工程学报

›› 2012, Vol. 48 ›› Issue (4): 40-43.

• 论文 • 上一篇    下一篇

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基于支持矢量机的疲劳应力集中系数预测模型研究

侯哲哲;杜彦良;赵维刚   

  1. 北京交通大学机械与电子控制工程学院;石家庄铁道大学材料科学与工程学院;石家庄铁道大学大型结构健康诊断与控制研究所
  • 发布日期:2012-02-20

Application of Support Vector Machine in the Prediction of Fatigue Stress Concentration Factor

HOU Zhezhe;DU Yanliang;ZHAO Weigang   

  1. School of Mechanical Electronic and Control Engineering, Beijing Jiaotong University School of Material Science and Engineering, Shijiazhuang Tiedao University Structure Health Monitoring and Control Institute, Shijiazhuang Tiedao University
  • Published:2012-02-20

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摘要： 疲劳应力集中系数作为材料疲劳抗力指标的疲劳极限，不仅反映了疲劳应力集中的程度，还反映了材料对缺口的敏感程度，将近年来飞速发展的支持矢量机(Support vector machine，SVM)应用于疲劳应力集中系数的研究。介绍支持矢量机的基本原理，利用LIBSVM，选择高斯型径向基函数(Radial basis function，RBF)作为核函数，建立以材料的抗拉强度、屈服强度、光棒疲劳强度、理论应力集中系数、缺口根部半径、试样尺寸及缺口疲劳极限作为输入值，疲劳应力集中系数为输出值的模型，从而对疲劳应力集中系数进行分析和预测。同时，SVM模型与经验公式Neuber式和Peterson式的计算值进行比较。结果表明，在小样本条件下，应用SVM技术构建的数学模型， 模型的拟合相对误差小于7.4%，从而证明该SVM模型的准确性和适用性。

关键词: 力学性能, 疲劳应力集中系数, 预测, 支持矢量机

Abstract: Fatigue stress concentration factors as fatigue limit of material fatigue resistance not only reflected the extent of fatigue stress concentration but also reflected for notch sensitive degree. The newly developed technical support vector machine(SVM) into the domain of fatigue stress concentration factor is introduced. Support vector machine theory is introduced, LIBSVM is used, and RBF is chosen as kernel function to build a mode. The input parameters are tensile strength, yield strength, fatigue strength, stress concentration factor，notch root radius, samples size and notch fatigue limit; Fatigue stress concentration factor is output. The calculations of SVM model, experimental formula Neural and Peterson are compared. The results show that the relative maximum predicting error is 0.7% under the condition of using a small quantity of samples to build the mathematical model. The accuracy and applicability for the present model are thus verified.

Key words: Fatigue stress concentration factor, Mechanics performance, Predict, Support vector machine

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