STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION

WANG XiaoQiang; RUAN XiaoLin; CUI FengKui; LIU Fei

doi:10.16579/j.issn.1001.9669.2020.04.008

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STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION

更新时间：2022-09-22

- STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION
- Journal of Mechanical Strength Vol. 42, Issue 4, Pages: 811-816(2020)
- 作者机构：
  
  1. 河南科技大学机电工程学院
  2. 河南省机械装备先进制造协同创新中心
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2020.04.008
  CLC：
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WANG XiaoQiang, RUAN XiaoLin, CUI FengKui, et al. STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION. [J]. 42(4):811-816(2020)
DOI：

WANG XiaoQiang, RUAN XiaoLin, CUI FengKui, et al. STUDY ON PREDICTION MODEL OF SURFACE HARDNESS IN ULTRASOUND ROLLING EXTRUSION. [J]. 42(4):811-816(2020) DOI： 10.16579/j.issn.1001.9669.2020.04.008.

摘要

表面硬度是评价表面加工质量的一项重要指标,超声滚挤压强化加工技术对于表面强化具有十分显著的作用。以42CrMo轴承钢为对象,对超声滚挤压正交实验结果进行了极差分析,得到了工艺参数对表面硬度的作用显著性大小,运用k折交叉验证法,充分考虑模型的拟合能力和预测能力,分段对比分析了BP神经网络模型和逐步回归模型的可靠度和精确性。结果表明,逐步回归模型的验证误差分布范围和平均误差更小,具有更高的预测精度;最终所建的表面硬度预测模型整体及系数显著性强,能够应用于超声滚挤压加工表面质量的优化提高研究中。

Abstract

Surface hardness is an important index for evaluating the quality of surface processing. Ultrasonic rolling and extrusion technology plays a very significant role in surface strengthening. Taking 42 CrMo bearing steel as the object,the range analysis was carried out on the orthogonal experiment results of ultrasonic rolling extrusion,and the significance of process parameters on surface hardness was obtained. The reliability and accuracy of BP neural network model and stepwise regression model were compared and analyzed in sections by using k-fold cross validation method. This process fully considers the fitting ability and prediction ability of the model. The results show that the validation error range and average error of the stepwise regression model are smaller,and the prediction accuracy of the model is higher. Finally,the established prediction model of surface hardness has strong overall and coefficient significance,which can be applied to the optimization and improvement of surface quality in ultrasonic rolling extrusion.

关键词

超声滚挤压BP神经网络逐步回归预测模型表面硬度

Keywords

Ultrasonic rolling extrusionBP neural networkStepwise regressionPrediction modelSurface Hardness

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