MMM Fatigue Damage Evaluation and Life Prediction Modeling for Ferromagnetic Materials

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Abstract:

The metal magnetic memory (MMM) technology, based on the magneto-elasticity and magneto-mechanical effect theory, has been applied to remaining fatigue life prediction. The correlation between fatigue life and MMM parameter has been investigated through rotary bending fatigue experiments. Steel X45 samples, with artificial cracks of different depth and breadth, are tested with MMM method. Based on the results of the metallographic examination, the feasibility of remaining fatigue life prediction is studied. A new remaining fatigue life MMM model of ferromagnetic material is presented. The proving experiments show the maximum error of remaining fatigue life is 4.58% between MMM model calculation and the actual life. The agreement of remaining fatigue life predicting values and testing values is found to be quite good.

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Key Engineering Materials (Volumes 324-325)

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619-622

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November 2006

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© 2006 Trans Tech Publications Ltd. All Rights Reserved

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[1] Dubov A.A. Study of metal properties using metal magnetic memory method [A]. 7th European Conference on Non-destructive Testing [C]. Copenhagen. 1997, (10): 920~927.

Google Scholar

[2] Dubov, A.A. Problems in estimating the remaining life of aging equipment [J]. Thermal Engineering. 2003, 50(11): 935~938.

Google Scholar

[3] Dong Lihong, Xu Binshi, Dong Shiyun. Metal magnetic memory testing for early damage assessment in ferromagnetic materials[J]. J. CENT. SOUTH UNIV. TECHNOL. 2005, 12(S2): 102~106 No. Testing� Nδ % Predicting Nδ % Error % No. Testing� Nδ %� Predicting Nδ % Error % 1 2 3 4 5 6 7 8 9 48 39 58 52 48 36 12 39 36.

DOI: 10.1007/s11771-005-0019-8

Google Scholar

[47] 12.

Google Scholar

[37] 26.

Google Scholar

[55] 23.

Google Scholar

[55] 36.

Google Scholar

[47] 56.

Google Scholar

[33] 24.

Google Scholar

[15] 69.

Google Scholar

[43] 58.

Google Scholar

[35] 36.

Google Scholar

[1] 74.

Google Scholar

[2] 77.

Google Scholar

[3] 36.

Google Scholar

[2] 76.

Google Scholar

[3] 69.

Google Scholar

[4] 58.

Google Scholar

36 10 11 12 13 14 15 16 17 18 30 26 19 14 17 16 15 16 19.

Google Scholar

[31] 25.

Google Scholar

[26] 78.

Google Scholar

[18] 36.

Google Scholar

[12] 68.

Google Scholar

[17] 03.

Google Scholar

[16] 35.

Google Scholar

[19] 87.

Google Scholar

[17] 63.

Google Scholar

[18] 52.

Google Scholar

[1] 25.

Google Scholar

[2] 68.

Google Scholar

[4] 13.

Google Scholar

[1] 63.

Google Scholar