Grinding Burn Detection Research Articles

Magnetic and acoustic sensors in the age of digitalization for industrial production

The challenges of digitalization are giving rise to new generations of measurement technology. This does not necessarily involve the use of new physical principles; rather, the importance of handling and generating valuable data has increased. We present a new measuring system that samples and digitizes analogue signals at 50 MHz with 24-bit conversion. Every 43 µs, a Fourier step is generated for 1,024 data points via an FPGA-based measurement card. The resulting spectral data diagram can then be electronically filtered to improve the signal-to-noise ratio. With this procedure, further possibilities have been created to improve data analysis and data handling, one of which is a parametric pattern recognition, which transfers selectable signals to a library based on the spectral data diagram and searches for them in future data using a similarity algorithm. The use of Python is another building block. With Python, machine learning methods can be transferred from training directly to the measurement system and can run there directly using the extensive library standards. Sensor-fusion is a module that allows analog data to be analyzed via the measurement card, but at the same time also links digital data with analog data by connecting DAQ modules from a third source as virtual ports and transmitting values to shared databases. This article shows the application of the described measurement concept using magneto-inductive sensors and piezoelectric sensors. Magnetic sensors react sensitively to material changes and can reliably determine mechanical parameters such as hardness, residual stresses and grain size. The mentioned software functions enable non-contact measurement of ferromagnetic materials at high throughput speeds. So far limited to laboratory applications, magnetic sensors can also be used safely in harsh industrial conditions. Applications include rapid differentiation of heat treatment conditions, detection of grinding burn and hardness measurements. Acoustic sensors are used in areas that are difficult to access or whose environmental conditions make the use of optical systems in particular difficult. The sensors can mainly detect cracks, frictional behaviour and wear quantities. Applications are in particular straightening processes of hardened steel components, wire drawing, welding, deep drawing and machining. In general, this method can also be used to monitor other sensors and thus raise them to a new level of evaluation technology and data analysis.

Detection methods and bearing failure characteristics analysis related to grinding burns

Grinding burn is one of the bearing failure causes for components. In order to quickly confirm that grinding burn is the cause of bearing failure, detection methods for the grinding burns and characteristics of bearing failure caused by grinding burns have been studied in this paper. The results show that cold acid pickling examination, microhardness test and Barkhausen noise analysis can be used in the detection of heavy grinding burns but have several disadvantages to hinder their application on the detection of slight grinding burns. A grinding burn detection procedure is recommended for the analysis of bearing failure induced by grinding burn. It has been found that the modes of bearing failure induced by grinding burns were contact fatigue spalling developed from the contact fatigue crack, the propagation mechanism of the contact fatigue crack was obtained. And the unique phenomenon of fracture or fragmentation with cracks distributed in the origin or surrounding the spalling pit of the grinding burn layer was also obtained. Based on the above results the future work on detection methods and influence on bearing failure mechanism and life of grinding burn are proposed.

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

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.

Detectability of Thermomechanical Surface Damages on Quenched and Tempered, Nitrided and Case-Hardened Steels by Barkhausen Noise Analysis

Abstract Magnetic Barkhausen noise analysis is an already industrially used method for the detection of grinding burn and offers several advantages over the established nital etching test with respect to objectivity, cost savings as well as occupational safety and environmental protection. Studies on the detectability of damages and influencing factors such as the condition of the surface zone prior to grinding have so far only existed in isolated cases and mostly limited to a few widely used case-hardened steels. For this reason, various steel grades used in particular in the aerospace sector were considered in this study. For one quenched and tempered, one nitrided and two case-hardened steels, the detectability of damages by means of Barkhausen noise and the influence of different heat treatment parameters were investigated. It was shown that the use of Barkhausen noise analysis is also possible on these steel grades and enables more reliable damage detection than nital etching.

Analysis of robustness and transferability in feature-based grinding burn detection

Analysis of robustness and transferability in feature-based grinding burn detection

Scalogram-Based Instantaneous Features of Acoustic Emission in Grinding Burn Detection

Time-frequency methods are effective tools in identifying the frequency content of a signal and revealing its timevariant features. This paper presents the use of instantaneous features (i.e., instantaneous energy and signal phase) of acoustic emission (AE) in the detection of thermal damage to the workpiece in grinding. The low-order frequency moments of a scalogram are used to obtain both the instantaneous energy and the mean frequency at which the signal phase is recovered. The grinding process is monitored using AE for a variety of operating conditions, including regular grinding, grinding at higher cutting speed and larger feed, and small dressing depth of cut. The instantaneous features extracted by the scalogram are compared with the results obtained by the empirical mode decomposition. It has been found that both the instantaneous energy and phase deviation indicate the presence of burn damage and serve as robust and reliable indicators, providing a basis for detecting the grinding burn.

In-process detection of grinding burn using machine learning

The improvement of industrial grinding processes is driven by the objective to reduce process time and costs while maintaining required workpiece quality characteristics. One of several limiting factors is grinding burn. Usually applied techniques for workpiece burn are conducted often only for selected parts and can be time consuming. This study presents a new approach for grinding burn detection realized for each ground part under near-production conditions. Based on the in-process measurement of acoustic emission, spindle electric current, and power signals, time-frequency transforms are conducted to derive almost 900 statistical features as an input for machine learning algorithms. Using genetic programming, an optimized combination between feature selector and classifier is determined to detect grinding burn. The application of the approach results in a high classification accuracy of about 99% for the binary problem and more than 98% for the multi-classdetection case, respectively.

Soft sensor approach based on magnetic Barkhausen noise by means of the forming process punch-hole-rolling

The relevance of the magnetic Barkhausen noise (MBN) and the non-destructive characterization of material properties in near surface layers, has increased in recent years.With the development of new signal processing techniques, the method was further developed into a powerful evaluation technique and is used in various areas of online and offline measurement. In addition to the established use in the detection of grinding burn, the method is increasingly used in the context of soft sensors for property controlled processes, due to its short analysis times. By a detailed description of a soft sensor concept for the novel forming process punch-hole-rolling this work focuses on the offline characterization of the process specific cause-effect relationships. This is done by analyzing the process interactions as well as the surface layer state by a metallographic investigation. Additionally a non-destructive characterization by means of MBN was done and correlated with the surface layer state. This provides important findings for the use of a MBN-sensor in a soft sensor concept and the potential integration into the forming process.

Fusion of Optical and Microfabricated Eddy-Current Sensors for the Non-Destructive Detection of Grinding Burn

Fusion of Optical and Microfabricated Eddy-Current Sensors for the Non-Destructive Detection of Grinding Burn

Design and validation of low-cost handling equipment for the use of Barkhausen Noise Testing in worm gears grinding burn detection

Worm gears in high-performance gearboxes are often exposed to extreme loads, which requires assuring high production quality. Therefore the most important final characteristics of the workpieces are provided by grinding. One of the most investigated issues of this machining method is grinding burns, who change locally the properties of the material. In many industrial applications, nital etching is used to detect grinding burn. Although it is considered a standard, new types of tests are of great interest in order to reduce costs and make online control possible. One of the Non-Destructive Testing methods is Barkhausen Noise Testing (BNT), which is utilized to assess changes in the surface layer of ferromagnetic materials, especially to monitor changes in their hardness and residual stresses. To obtain accurate measures using this technology the Barkhausen probe handling system is essential. Typically the sensor handling system is an Inline/robot component, an automated component, a semi-automated component, or a manual handling system. Considering this scenario, the use of a manual system could allow having important cost savings but has to be correctly developed to obtain reliable measures. The aim of this paper is to develop a handling equipment for the BNT probe able to assure an adequate level of accuracy and repeatability.

Scalogram-Based Instantaneous Features of Acoustic Emission in Grinding Burn Detection

Time-frequency methods are effective tools in identifying the frequency content of a signal and revealing its time-variant features. This paper presents the use of instantaneous features (i.e. instantaneous energy and signal phase) of acoustic emission (AE) in the detection of thermal damage to the workpiece in grinding. Both the instantaneous energy and mean frequency are obtained using the low-order frequency moments of a scalogram. While the zero-order frequency moment yields the instantaneous energy, the first-order frequency moment gives the instantaneous frequency by which the signal phase is recovered. The grinding process is monitored using acoustic emission for various operating conditions, including the regular grinding, grinding at a higher cutting speed and larger infeed, and small dressing depth of cut. It has been found that both the instantaneous energy and phase deviation indicate the presence of burn damage and serve as robust and reliable indicators, providing a basis for detecting the grinding burn.

An approach for a reliable detection of grinding burn using the Barkhausen noise multi-parameter analysis

High loaded, case-hardened workpieces are often finished by grinding to meet required form tolerances and generate high-quality surfaces. The thermo-mechanical load during grinding influences the surface integrity and therefore the functional behavior of the workpieces. To increase productivity, grinding processes are often operated at their limit. Therefore, already even minor deviations from the desired process conditions can lead to thermo-mechanical damages, often referred to as grinding burn. Micromagnetic testing methods allow for a nondestructive analysis of the surface integrity of ground workpieces. Often, a combination of multiple parameters is used to get distinct results. In this study, the Barkhausen noise measurement was used for a nondestructive characterization of process specific influences on the surface integrity in order to further improve the grinding burn detection. Case-hardened workpieces made of AISI 4820 steel were finished by outer diameter grinding with varying specific material removal rates Q’w in a range of 0.5 mm³/(mm s) to 24 mm³/(mm s). Determining the resulting surface integrity, different levels of thermo-mechanical influence on the workpiece surface layer were detected. A detailed analysis of the effective Barkhausen noise (RMS-value), the Coercivity and the Peak Position was performed in order to evaluate the influence of the magnetization parameters on the resulting signals depending on the surface integrity. A varying sensitivity for indication of thermo-mechanical damages with different magnetization parameters was found. The presented results allow for an improvement of the reliability of the Barkhausen noise multi-parameter grinding burn analysis.

Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal

Grinding burn monitoring is of great importance to guarantee the surface integrity of the workpiece. Existing methods monitor overall signal variation. However, the signals produced by metal burn are always weak. Therefore, the detection rate of grinding burn still needs to be improved. The paper presents a novel grinding burn detection method basing on acoustic emission (AE) signals. It is achieved by establishing the coherence relationship of pure metal burn and grinding burn signals. Firstly, laser and grinding experiments were carried out to produce pure metal burn signals and grinding burn signals. No-burn and burn surfaces were generated and AE signals were captured separately. Then, the cross wavelet transform (XWT) and wavelet coherence (WTC) were applied to reveal the coherence relationship of the pure metal burn signal and grinding burn signal. The methods can reduce unwanted AE sources and background noise. Novel parameters based on XWT and WTC are proposed to quantify the degree of coherence and monitor the grinding burn. The grinding burn signals were recognized successfully by the proposed indexes under same grinding condition.

An intelligent grinding burn detection system based on two-stage feature selection and stacked sparse autoencoder

Grinding burn is a typical quality defect that negatively affects microstructures and mechanical properties of parts. In this article, an intelligent grinding burn detection system that consists of processing of grinding signals, extraction of signal features, selection of optimal feature subset, and classification of grinding burn is presented. During the grinding of maraging steel 3J33, physical signals are collected by a dynamometer, accelerometer, and acoustic emission sensor. A large amount of preliminary features in time domain and frequency domain are extracted after wavelet packet decomposition and ensemble empirical mode decomposition. To reduce the dimensionality of feature space and increase relevancy of feature to grinding burn, a two-stage feature selection strategy combining filter and wrapper method is proposed. The filter ranks individual features by ReliefF, while the wrapper evaluates different feature subsets according to the model performance. A deep learning network, stacked sparse autoencoder (SSAE), is adopted to develop the classification model of grinding burn and is compared with other machine learning models. The results demonstrate that the two-stage feature selection method is able to provide the optimal feature subset for the model and SSAE model obtains the best training and testing classification rate of grinding burn with least features among all comparative models.

Application of Hall effect for assessing grinding thermal damage

The limitations of the usual techniques applied for gear grinding burn detection are extended by the increasing demands on quality control within industrial feasibility. Although widely used, nital etching technique has a subjective character. Alternative techniques such as magnetic Barkhausen noise and X-ray diffractometry also present drawbacks such as variability of output based on external energy input that depends on parameter settings, high cost and destruction of tests parts. Due to this technical gap, an alternative method is suggested, where the remanent induction of a workpiece is measured by means of a Hall probe. The objective of this concept is to reduce external inputs on the workpiece, measuring its natural signal and correlating it to the material property induced by the damage. In this study, three different degrees of damage were investigated with the Hall method, covering damages with phase transformation as well as with modifications on the residual stress state. According to the results obtained, an alteration in the remanent induction which is directly correlated to the burn degree, was detected. The micro-magnetic theory establishes a correlation between the remanent magnetic induction and material hardness as well as residual stress state. The signal alteration detected by the Hall method is in accordance with the micro-magnetic theory for all three degrees of burn analyzed.

Grinding burn inspection

Grinding burn occurs during the grinding process of hardened steel surfaces if a local heat impact is high enough to generate local tempered or even re-hardened zones. Different NDT methods are applied to detect grinding burn. This lecture shares experiences and results of manufacturing and assessment of reference blocks with defined artificial defects generated by laser treatment for grinding burn detection. The reference blocks can be used for electromagnetic testing methods as well as for surface temper etching (STE). These blocks are used for calibration of the test equipment, especially to verify the sensitivity before testing real parts. In addition, reference blocks are applied in certain intervals within the process in order to guarantee testing reliability.

Detection of grinding burn through the high and low frequency Barkhausen noise

Rad razmatra pojavu ostecenja povrsine nakon brusenja putem nedestruktivnog mikro magnetskog nacina rada temeljenog na Barkhausenovom sumu. Uspoređuju se metode visoke i niske frekvencije i njihova osjetljivost u otkrivanju povrsinske opekline i debljine zone pod utjecajem topline. Rezultati istraživanja pokazuju da se metodom niske frekvencije može jasnije otkriti povrsinska opeklina nego metodom visoke frekvencije zbog relativno velike debljine zona pod utjecajem topline nastalih nekon brusenja. U radu se također raspravlja o dodatnim aspektima mikro magnetskog ispitivanja kao multi parametrijske metode u kojoj su parametri kao sto je vrsni položaj petlje Barkhausenovog suma u korelaciji s toplinskim opterecenjem obrađivane povrsine. U radu se također daju odgovarajuce promjene u strukturi i s tim povezano naprezanje ispod povrsine.

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

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 \(\upmu \)m in the range between 150 and 2500 \(\upmu \)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.

Application of micro-magnetic testing systems for non-destructive analysis of wear progress in case-hardened 16MnCr5 gear wheels

Abstract Micro-magnetic testing methods are qualified for non-destructive quantification of hardness, hardness depth and residual stresses. Among others they are applied for detection of grinding burn in gear wheels, but an application for wear condition monitoring has not yet been published. In this paper, results of initial research of determination of wear condition in gear wheels by application of micro-magnetic testing systems are presented. For comparison of different wear conditions, gears were loaded for increasing numbers of cycles in a test rig based on FVA information sheet 54/7 and DIN ISO 14635 part 1. Operating conditions were altered by usage of different lubricants. Afterwards, wear conditions were determined by conventional techniques, i. e., measuring change in profile and loss of material. Four measurement principles were evaluated for change in micro-magnetic properties determination, magnetic Barkhausen noise analysis, permeability and eddy-current measurements, as well as harmonic analysis of tangential field strength. A general suitability of micro-magnetic testing approach for characterization of wear condition of gear wheels could be demonstrated by comparison of micro-magnetic properties with common wear indicators. Micro-magnetic properties were not solely influenced by wear condition, as the selected oil, and hence the tribochemical conditions in contact also showed a significant effect on measured values. Therefore, further survey is required for direct correlation of micro-magnetic properties with (micro-)structural material changes.

Development of Non-Destructive Inspection System for Grinding Burn-in-Process Detection of Grinding Burn

This study was performed to develop a non-destructive inspection system to detect grinding burn, which is capable of quantitative 100% inspection inside a production line. An eddy current sensor, which has advantages of short inspection time and low cost, was used. It was shown that the grinding burn detection technique had been developed and is possible to detect grinding burn by using this technique, in the 1st report1) and the 2nd report2). In this report, an experiment of in-process detection of grinding burn was conducted, by applying the grinding burn detection technique which we have developed. The eddy current sensor has been combined with an in-process gauge in order to keep the clearance between the sensor probe and the work piece constant. It was shown that grinding burn can be detected successfully during cylindrical machining.