University of Oulu

Sorsa, A., Santa-aho, S., Aylott, C., Shaw, B., Vippola, M., & Leiviskä, K. (2019). Case Depth Prediction of Nitrided Samples with Barkhausen Noise Measurement. Metals, 9(3), 325.

Case depth prediction of nitrided samples with barkhausen noise measurement

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Author: Sorsa, Aki1; Santa-aho, Suvi2; Aylott, Christopher3;
Organizations: 1Control Engineering, Environmental and Chemical Engineering research unit, University of Oulu, 90014 Oulu, Finland
2Materials Science and Environmental Engineering research unit, Tampere University, 33014 Tampere, Finland
3Design Unit, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.9 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2019
Publish Date: 2019-09-18


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.

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Series: Metals
ISSN: 2075-4701
ISSN-E: 2075-4701
ISSN-L: 2075-4701
Volume: 9
Issue: 3
Article number: 325
DOI: 10.3390/met9030325
Type of Publication: A1 Journal article – refereed
Field of Science: 216 Materials engineering
213 Electronic, automation and communications engineering, electronics
Funding: This research was funded by the Academy of Finland, project FUNBARK.
Copyright information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (