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Metals 2019, 9(3), 325; https://doi.org/10.3390/met9030325

Case Depth Prediction of Nitrided Samples with Barkhausen Noise Measurement

1
Control Engineering, Environmental and Chemical Engineering research unit, University of Oulu, 90014 Oulu, Finland
2
Materials Science and Environmental Engineering research unit, Tampere University, 33014 Tampere, Finland
3
Design Unit, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
*
Author to whom correspondence should be addressed.
Received: 13 February 2019 / Revised: 8 March 2019 / Accepted: 10 March 2019 / Published: 14 March 2019
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Abstract

Nitriding is a heat treatment process that is commonly used to enhance the surface properties of ferrous components. Traditional quality control uses sacrificial pieces that are destructively evaluated. However, efficient production requires quality control where the case depths produced are non-destructively evaluated. In this study, four different low alloy steel materials were studied. Nitriding times for the samples were varied to produce varying case depths. Traditional Barkhausen noise and Barkhausen noise sweep measurements were carried out for non-destructive case depth evaluation. A prediction model between traditional Barkhausen noise measurements and diffusion layer hardness was identified. The diffusion layer hardness was predicted and sweep measurement data was used to predict case depths. Modelling was carried out for non-ground and ground samples with good results. View Full-Text
Keywords: Barkhausen noise; magnetic methods; material characterization; nitriding; mathematical modelling; signal processing Barkhausen noise; magnetic methods; material characterization; nitriding; mathematical modelling; signal processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sorsa, A.; Santa-aho, S.; Aylott, C.; Shaw, B.A.; Vippola, M.; Leiviskä, K. Case Depth Prediction of Nitrided Samples with Barkhausen Noise Measurement. Metals 2019, 9, 325.

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