Case depth evaluation of induction-hardened camshaft by using magnetic Barkhausen noise (MBN) method
Artikel i vetenskaplig tidskrift, 2021

Magnetic Barkhausen noise (MBN) method is one of the non-destructive evaluating (NDE) techniques used in industry to monitor the quality of ferromagnetic products during manufacture. In this article, case depth evaluation of the camshaft lobes by this means after induction hardening is described. A routine industrial monitoring practice is found to have limitation to evaluate the thickness of this process-hardened layer. With the aid of metallography on selected samples, this uppermost layer is found to have one, or more than one microconstituents. This infers that each type possesses different physical properties in response to the MBN measurement. Consequently, the interpretation of the MBN signal/data for case depth evaluation is not straight-forward. From metallography, a qualified component should have a uniform layer of martensite with grains ≤ 50 µm and the thickness around 3.0–5.0 mm. This gives the magnetoelastic parameter (i.e. mp) in a range of 20–70 in industrial MBN measurement. The mp outside this range corresponds to either a non-martensitic type or a martensitic type with grains > 50 µm. In fact, the characteristic features of a Barkhausen burst like peak intensity, width and position can be used to categorise different microstructural conditions. Then, the case depth of the qualified components, or the thickness of the qualified martensite, can be estimated. Statistical regression decision tree model helps to divide this qualified group into three sub-groups between 3.0 and 6.0 mm, and each can be identified by the decision criteria based on the specific ranges of the mp reading, the RMS of peak intensity and the peak position. In the end, a physical model is used to show how the difference of microstructures is influencing the magnetic flux, and thus the mp. Nevertheless, more information is needed to improve the model for this application. ​

Magnetic Barkhausen noise


multivariable analysis

physical modelling

Induction-hardened carbon steel


Eric Tam

Chalmers, Industri- och materialvetenskap, Material och tillverkning

Peter Hammersberg

Chalmers, Industri- och materialvetenskap, Konstruktionsmaterial

Gert Persson

Chalmers, Industri- och materialvetenskap, Konstruktionsmaterial

Jari Olavison

Volvo Group

Nondestructive Testing and Evaluation

1058-9759 (ISSN)

Vol. 36 5 494-514

FFI OFP4p Oförstörande karakteriseringskoncept för produktion

VINNOVA (2015-03721), 2015-11-02 -- 2018-11-30.





Metallurgi och metalliska material



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