Induction Hardened Layer Characterization and Grinding Burn Detection by Magnetic Barkhausen Noise Analysis

Lasaosa, Aitor; Gurruchaga, Kizkitza; Arizti, F.; Martínez-de-Guerenu, A.

doi:10.1007/s10921-016-0388-y

Cited by 26 publications

(17 citation statements)

References 21 publications

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“…For example, in surface-hardened components, where two layers of different phases are present (e.g., martensite in the outer layer and ferrite, pearlite or bainite phases at the core), the MBN envelope is also affected by the depth of the hard surface layer, and this complicates the use of generic threshold values to detect grinding burns in ball screw shafts of different hardening depths due to variations in heat treatments. In our previous works [17,18], in which we obtained a two-peak MBN envelope, the value of the magnetic field at the second peak (H2) is not greatly influenced by the different heat treatments, and therefore not by the hardened layer depth. The parameter H2 moves to lower intensity values of the magnetic field (Ht) in the ball screw shafts in which grinding burns have been produced.…”

Section: Introductionmentioning

confidence: 69%

“…The induction hardening layer depth (LD) was defined as the depth at which the microhardness falls below 500 HV. Hardness profiles and surface hardness of 14 different induction hardening treatment batches were detailed in [17]. In the present paper, specific attention will be firstly given to a small number of these batches (e.g., T3, T10 and T12 refer to the heat treatment batch number in [17] and sample numbers 1 and 2 will be referred to as T3 S1 and T3 S2).…”

Section: Methodsmentioning

confidence: 99%

“…Hardness profiles and surface hardness of 14 different induction hardening treatment batches were detailed in [17]. In the present paper, specific attention will be firstly given to a small number of these batches (e.g., T3, T10 and T12 refer to the heat treatment batch number in [17] and sample numbers 1 and 2 will be referred to as T3 S1 and T3 S2). Secondly, results of a larger number of hardening treatment batches with unknown LD will be studied.…”

Section: Methodsmentioning

confidence: 99%

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Grinding Burn Detection via Magnetic Barkhausen Noise Analysis Independently of Induction Hardened Depth

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Grinding Burn Detection via Magnetic Barkhausen Noise Analysis Independently of Induction Hardened Depth

et al. 2023

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“…Those methods are cheap, non-polluting, and can easily be set to perform reproducible tests on production lines. Different ways exist, but the most popular ones are based on the so-called Magnetic Barkhausen Noise (MBN) analysis [ 12 , 13 , 14 ]. A set of industrial equipment, such as the popular Stresstech ® controller, based on this peculiar magnetic manifestation has already been developed [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Signatures and Magnetization Mechanisms for Grinding Burns Detection and Evaluation

Ducharne,

Sebald,

Petitpré

et al. 2023

Sensors

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Grinding thermal damages, commonly called grinding burns occur when the grinding energy generates too much heat. Grinding burns modify the local hardness and can be a source of internal stress. Grinding burns will shorten the fatigue life of steel components and lead to severe failures. A typical way to detect grinding burns is the so-called nital etching method. This chemical technique is efficient but polluting. Methods based on the magnetization mechanisms are the alternative studied in this work. For this, two sets of structural steel specimens (18NiCr5-4 and X38Cr-Mo16-Tr) were metallurgically treated to induce increasing grinding burn levels. Hardness and surface stress pre-characterizations provided the study with mechanical data. Then, multiple magnetic responses (magnetic incremental permeability, magnetic Barkhausen noise, magnetic needle probe, etc.) were measured to establish the correlations between the magnetization mechanisms, the mechanical properties, and the grinding burn level. Owing to the experimental conditions and ratios between standard deviation and average values, mechanisms linked to the domain wall motions appear to be the most reliable. Coercivity obtained from the Barkhausen noise, or magnetic incremental permeability measurements, was revealed as the most correlated indicator (especially when the very strongly burned specimens were removed from the tested specimens list). Grinding burns, surface stress, and hardness were found to be weakly correlated. Thus, microstructural properties (dislocations, etc.) are suspected to be preponderant in the correlation with the magnetization mechanisms.

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“…However, the test is not applicable to online CD measurements for expensive and large components because it is time-consuming and destructive. Therefore, the necessity for non-destructive testing and evaluation (NDT&E) methods for measurement of CD in surface-hardened steel is recognized [3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An intelligent approach for simultaneously performing material type recognition and case depth prediction in two types of surface-hardened steel rods using a magnetic hysteresis loop

Zhu

Sun

2019

Meas. Sci. Technol.

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In traditional methods for measuring case depths (CDs) in two types of surface-hardened steels, two prediction models are generally established. However, it is difficult to recognize material types (MTs) of surface-hardened steels based on the appearance of steels, thus leading to difficulty in the selection of an appropriate model from the two obtained models for predicting CD in a given surface-hardened steel. The established model should allow both the prediction of the CDs in two types of surface-hardened steels and MT recognition. In this study, an intelligent approach is proposed to automatically establish a prediction model for simultaneously performing MT recognition and CD prediction in two types of materials. The intelligent approach involves sample preparation, magnetic hysteresis loop (MHL) measurements, feature generation, feature selection, feature extraction and prediction model establishment. In the feature generation process, the entire feature set is generated from measured MHL signals. In the feature selection process, the binary-bat-algorithm-based (BBA) feature selection is firstly repeated 50 times to select feature subsets from the entire feature set. Then, a threshold criterion is proposed to extract suitable features from the repeated feature selection results in the feature extraction step. Finally, a modified neural network is proposed for predictions. The experimental results showed that the extracted features could give richer descriptions of the MHL signals, as well as the properties of steels. The established prediction model showed good performance in simultaneously performing MT recognition and CD prediction in two types of materials. The prediction error of case depth (PECD), misclassification rate (MR) and test time were mm (3.47%), 0% and 0.0105 s, respectively, demonstrating that the proposed approach was applicable for on-line CD measurements in two types of surface-hardened samples.

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Induction Hardened Layer Characterization and Grinding Burn Detection by Magnetic Barkhausen Noise Analysis

Cited by 26 publications

References 21 publications

Grinding Burn Detection via Magnetic Barkhausen Noise Analysis Independently of Induction Hardened Depth

Grinding Burn Detection via Magnetic Barkhausen Noise Analysis Independently of Induction Hardened Depth

Magnetic Signatures and Magnetization Mechanisms for Grinding Burns Detection and Evaluation

An intelligent approach for simultaneously performing material type recognition and case depth prediction in two types of surface-hardened steel rods using a magnetic hysteresis loop

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