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考虑仪器重复性的 Cr12MoV 钢表面硬度的磁学和稳健评估。

Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument.

机构信息

Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.

出版信息

Sensors (Basel). 2023 Jan 22;23(3):1273. doi: 10.3390/s23031273.

Abstract

The combination of multifunctional micromagnetic testing and neural network-based prediction models is a promising way of nondestructive and quantitative measurement of steel surface hardness. Current studies mainly focused on improving the prediction accuracy of intelligent models, but the unavoidable and random uncertainties related to instruments were seldom explored. The robustness of the prediction model considering the repeatability of instruments was seldom discussed. In this work, a self-developed multifunctional micromagnetic instrument was employed to perform the repeatability test with Cr12MoV steel. The repeatability of the instrument in measuring multiple magnetic features under both static and dynamic conditions was evaluated. The magnetic features for establishing the prediction model were selected based on the consideration of both the repeatability of the instrument and the ability of magnetic features in surface hardness evaluation. To improve the robustness of the model in surface hardness prediction, a modelling strategy considering the repeatability of the instrument was proposed. Through removing partial magnetic features with higher mean impact values from input nodes, robust evaluation of surface hardness in Cr12MoV steel was realized with the multifunctional micromagnetic instrument.

摘要

多功能磁强计与基于神经网络的预测模型相结合,是一种有前途的非破坏性、定量测量钢表面硬度的方法。目前的研究主要集中在提高智能模型的预测精度上,但很少探讨与仪器相关的不可避免且随机的不确定性。考虑到仪器重复性的预测模型的稳健性很少被讨论。在这项工作中,采用了自行开发的多功能磁强计对 Cr12MoV 钢进行重复性测试。评估了仪器在静态和动态条件下测量多个磁特性的重复性。基于对仪器重复性和磁特性在表面硬度评估能力的考虑,选择了用于建立预测模型的磁特性。为了提高模型在表面硬度预测中的稳健性,提出了一种考虑仪器重复性的建模策略。通过从输入节点中删除具有较高平均影响值的部分磁特征,多功能磁强计实现了对 Cr12MoV 钢表面硬度的稳健评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fff/9921488/df32c7b2ce48/sensors-23-01273-g001.jpg

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