Nonlinear Eddy Current Technique for Characterizing Case Hardening Profiles

被引:13
作者
Chan, Shiu Chuen [1 ]
Grimberg, Raimond [2 ]
Hejase, Jose A. [1 ]
Zeng, Zhiwei [3 ]
Lekeakatakunju, Peter [1 ]
Udpa, Lalita [1 ]
Udpa, Satish S. [1 ]
机构
[1] Michigan State Univ, Dept Elect & Comp Engn, E Lansing, MI 48824 USA
[2] Natl Inst R&D Tech Phys, Iasi, Romania
[3] Xiamen Univ, Dept Aeronaut, Xiamen 361005, Fujian, Peoples R China
关键词
Automotive bearing assembly; case hardening profile evaluation; Iterative Dichotomiser 3 (ID3) algorithm; neural network; nondestructive testing; nonlinear eddy current;
D O I
10.1109/TMAG.2010.2044980
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Industrial components are often case hardened to improve their strength and wear characteristics. Traditionally, component samples are collected from the production line at specific intervals and destructively tested for case depth profile assessment. This process is time-consuming, laborious, and can potentially allow an improperly treated component to escape detection. This paper presents a novel nonlinear eddy current technique for assessing the case hardening profile based on the premise that the magnetic characteristic of the case hardened region is different from that of the host material. A custom electromagnetic excitation-sensor array is used to both apply sinusoidal excitations to the component and measure the nonlinear response at multiple excitation frequencies and spatial locations, taking advantage of the different penetration regions due to the skin depth phenomenon. Each response signal obtained from the component under test is compared with that from a reference component subjected to the same excitation. Two pattern recognition algorithms (an artificial neural network and the Iterative Dichotomiser 3 (ID3) algorithm) are then used to process selected characteristics of the difference signal to determine the case depth profile of the component. The nonlinear eddy current technique has been applied to evaluate the case hardening profile of automotive bearing assemblies. This problem is challenging due to the variations in geometry across assemblies as well as the limited accessibility to the case hardened surface. In the neural network test, the system achieved a 95.77% accuracy. For the ID3 algorithm, the system achieved a 95.65% accuracy. These results demonstrate that the nonlinear eddy current inspection technique is highly promising in characterizing the case profile of induction hardened parts.
引用
收藏
页码:1821 / 1824
页数:4
相关论文
共 7 条
[1]  
[Anonymous], 2014, C4. 5: programs for machine learning
[2]   Artificial neural networks: A tutorial [J].
Jain, AK ;
Mao, JC ;
Mohiuddin, KM .
COMPUTER, 1996, 29 (03) :31-+
[3]   Reconstruction of depth profiles of thermal conductivity of case hardened steels using a three-dimensional photothermal technique [J].
Qu, Hong ;
Wang, Chinhua ;
Guo, XinXin ;
Mandelis, Andreas .
JOURNAL OF APPLIED PHYSICS, 2008, 104 (11)
[4]  
Quinlan J. R., 1986, Machine Learning, V1, P81, DOI 10.1007/BF00116251
[5]  
VENGRINOVICH V, 1992, RES NONDESTRUCTIVE E, V4, P96101
[6]   Magneto-Acoustic Emission and Magnetic Barkhausen Emission for Case Depth Measurement in En36 Gear Steel [J].
Wilson, John W. ;
Tian, Gui Yun ;
Moorthy, Vaidhianathasamy ;
Shaw, Brian A. .
IEEE TRANSACTIONS ON MAGNETICS, 2009, 45 (01) :177-183
[7]  
Xiang D, 2000, AIP CONF PROC, V509, P1471