The electromagnetic technique based on magnetic Barkhausen noise (MBN) can be used to control the quality of ball screw shafts non-destructively, although identifying any slight grinding burns independently of induction-hardened depth remains a challenge. The capacity to detect slight grinding burns was studied using a set of ball screw shafts manufactured by means of different induction hardening treatments and different grinding conditions (some of them under abnormal conditions for the purpose of generating grinding burns), and MBN measurements were taken in the whole group of ball screw shafts. Additionally, some of them were tested using two different MBN systems in order to better understand the effect of the slight grinding burns, while Vickers microhardness and nanohardness measurements were taken in selected samples. To detect the grinding burns (both slight anddata intense) with varying depths of the hardened layer, a multiparametric analysis of the MBN signal is proposed using the main parameters of the MBN two-peak envelope. At first, the samples are classified into groups depending on their hardened layer depth, estimated using the intensity of the magnetic field measured on the first peak (H1) parameter, and the threshold functions of two parameters (the minimum amplitude between the peaks of the MBN envelope (MIN) and the amplitude of the second peak (P2)) are then determined to detect the slight grinding burns for the different groups.

In this work, two different parametric hysteresis models, the Jiles-Atherton model and the Mel'gui relation, have been combined to form a more general hysteresis operator, suitable for the description of families of experimental B(H) curves obtained for low carbon (LC) steel specimens after isothermal annealing at different temperatures and times. As it has been demonstrated in a number of previous studies, characteristic values of steel hysteresis curves can be used as very efficient identifiers for the monitoring of the different metallurgical transformations that take place during the annealing, such as recovery and recrystallisation processes. It is thus important from a practical point to be able to reproduce the experimental curves obtained under different conditions, as precisely as possible, in order to proceed to the samples characterisation. Hybridisation of the two aforementioned models demonstrated satisfactory results for the reproduction of all considered curves obtained under the different considered annealing conditions.

Using nondestructive techniques to quantitatively estimate residual stresses along the depth is necessary to improve the ability to predict the real fatigue life of pieces for many applications. Magnetic Barkhausen noise has been proven to successfully estimate the residual stress at the surface produced by machining, plastic deformation, phase transformation or surface treatments such as shot peening, also allowing one to obtain information of the residual stress depth-profile in shot peened pieces which presented similar depth-profile shapes. However, residual stress depth-profiles with nonmonotonic or different shapes have not been successfully estimated. In the present study, an extended approach is developed in order to estimate these stresses independent of the shape of the residual stress depth-profile. The approach proposed here improves an existing model of the Barkhausen noise spectrum (Kypris-Jiles model) by adding the effect of the attenuation of the applied magnetic field on the Barkhausen noise. This extended approach is used to estimate the residual stress depth-profiles of samples with different depth-profiles using a calibration process. The approach is validated by estimating the residual stress depth-profiles, with errors smaller than 70 MPa in a depth of 130 mu m, in all the samples studied.

The meta-modelling approach based on an adaptive sparse grid interpolator is proposed for tackling the identification problem of parametric hysteresis models for steels with different microstructures. Parametric models of Jiles-Atherton and Mel'gui, respectively, have been considered in this work. The main advantage of the present approach is the separation of the calculation procedure in a computationally demanding off-line phase, which has to be carried out only once, and a very fast on-line evaluation. This decomposition is particularly interesting when a large amount of successive evaluations has to be carried out. Especially in the case that we are interested in a particular family of ferromagnetic materials (e.g. steels subjected to different treatments), where the sought parameters are lying in a specific interval, a single meta-model may be sufficient to be used for the study of a wide range of specimens. The steel samples considered in this study have been obtained from industrially produced low carbon steel, 84% cold rolled, and isothermally annealed in laboratory.

The quality of the ball screw shafts used in the aeronautical sector has to be controlled and certified with the most advanced non-destructive techniques. The capacity of magnetic Barkhausen noise(MBN) as a non-destructive technique to control the quality of ball screw shafts by assuring the appropriate induction hardened layer depth and detecting local overheated regions, known as grinding burns, which may occur during grinding processes is shown in the present work. Magnetic Barkhausen noise measurements were made with a system designed and implemented by the authors and the derived parameters were compared with microhardness measurements made at various depths after the different induction hardening treatments and the grinding processes were applied. A multiparametric study of the MBN signal as a function of the magnetic field in the surface of the sample is done in order to estimate the thickness of the hardened layer and to detect the grinding burns produced during grinding processes. The hardened layer thickness can be characterized with an error of +/- 200 mu m in the range between 150 and 2500 mu m by the position of the first peak of the MBN envelope in terms of the tangential magnetic field measured at the surface and the grinding burns can be detected with the position of the second peak of the MBN envelope in terms of the tangential magnetic field measured at the surface.

The use of magnetic Barkhausen noise (MBN) signal to non-destructively characterize the in-depth residual stress state of machined steel was investigated. The effect of the frequency of the magnetic field applied and of analysing the resulting MBN signal in different frequency bands for an in-depth residual stress characterisation is discussed. The effect of the residual stress on each of the parameters derived from the MBN signal is analysed comparing with the result of the XRD method.