Feasibility of studying 460 MPa high-strength steel affected by strain aging with acoustic emission method

2020 ◽  
pp. 136943322095061
Author(s):  
Yiting Yang ◽  
Yan Wang ◽  
Kehao Li
Keyword(s):  
Acoustic Emission ◽  
High Strength Steel ◽  
Strain Aging ◽  
High Strength ◽  
Tensile Tests ◽  
Aging Effects ◽  
Time Frequency ◽  
Frequency Domains ◽  
Ae Signals ◽  
Partial Damage

Strain aging significantly influences the behavior of partially damaged structural steel. The effects of strain aging can be determined based on mechanical experiments, but the sampling process causes further damage to the structures. To explore the possibility of using nondestructive testing (NDT) technology to distinguish the strain aging effects on steel materials, this study investigates the characteristics of acoustic emission (AE) signals for high-strength steel (HSS) affected by strain aging. First, different strain aging effects were applied to 460 MPa HSS specimens. Second, pencil lead break (PLB) tests were performed on the specimens with strain aging, and the generated AE signals were recorded. Finally, tensile tests were conducted, that the strain aging effects on mechanical behavior were determined. The obtained AE signals were compared by extracting the AE parameters and analyzed in the frequency and time-frequency domains with a fast Fourier transform (FFT) and a wavelet transform (WT), respectively. The study shows that the strain aging effects change the characteristics of the AE signals. Compared to the prestrain, the aging time has a more pronounced impact on the AE behaviors. This research proposes a possible NDT method to determine the effects of strain aging on steel materials. Experimental data are provided to detect the degree of partial damage to 460 MPa HSS owing to strain aging using the AE method.

scholarly journals Experimental Investigation into Corrosion Effect on Mechanical Properties of High Strength Steel Bars under Dynamic Loadings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hui Chen ◽  
Jinjin Zhang ◽  
Jin Yang ◽  
Feilong Ye
Keyword(s):  
Mechanical Properties ◽  
Strain Rate ◽  
Dynamic Loading ◽  
High Strength Steel ◽  
High Strength ◽  
Tensile Tests ◽  
Reinforcing Steel ◽  
Test Results ◽  
Cross Sectional ◽  
Steel Bars

The tensile behaviors of corroded steel bars are important in the capacity evaluation of corroded reinforced concrete structures. The present paper studies the mechanical behavior of the corroded high strength reinforcing steel bars under static and dynamic loading. High strength reinforcing steel bars were corroded by using accelerated corrosion methods and the tensile tests were carried out under different strain rates. The results showed that the mechanical properties of corroded high strength steel bars were strain rate dependent, and the strain rate effect decreased with the increase of corrosion degree. The decreased nominal yield and ultimate strengths were mainly caused by the reduction of cross-sectional areas, and the decreased ultimate deformation and the shortened yield plateau resulted from the intensified stress concentration at the nonuniform reduction. Based on the test results, reduction factors were proposed to relate the tensile behaviors with the corrosion degree and strain rate for corroded bars. A modified Johnson-Cook strength model of corroded high strength steel bars under dynamic loading was proposed by taking into account the influence of corrosion degree. Comparison between the model and test results showed that proposed model properly describes the dynamic response of the corroded high strength rebars.

scholarly journals Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning

Lubricants ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 29 ◽  
Author(s):  
Noushin Mokhtari ◽  
Jonathan Gerald Pelham ◽  
Sebastian Nowoisky ◽  
José-Luis Bote-Garcia ◽  
Clemens Gühmann
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Friction And Wear ◽  
Journal Bearing ◽  
Journal Bearings ◽  
Wear Volume ◽  
Time Frequency ◽  
Ae Signal ◽  
Ae Signals

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.

scholarly journals Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

2020 ◽  
Vol 10 (20) ◽  
pp. 7068
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Extraction Methods ◽  
High Accuracy ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

scholarly journals Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 991 ◽  
Author(s):  
Md Hasan ◽  
Jong-Myon Kim
Keyword(s):  
Fault Detection ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Reduction Techniques ◽  
Spherical Tank ◽  
Frequency Domains ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction ◽  
Ae Signals

Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts: (1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.

An intelligent prediction model of the tool wear based on machine learning in turning high strength steel

2020 ◽  
Vol 234 (13) ◽  
pp. 1580-1597
Author(s):  
Minghui Cheng ◽  
Li Jiao ◽  
Xuechun Shi ◽  
Xibin Wang ◽  
Pei Yan ◽  
...  
Keyword(s):  
Tool Wear ◽  
Prediction Model ◽  
Cutting Force ◽  
Surface Quality ◽  
Surface Texture ◽  
High Strength Steel ◽  
Prediction Models ◽  
High Strength ◽  
Support Vector ◽  
Time Frequency

In the process of high strength steel turning, tool wear will reduce the surface quality of the workpiece and increase cutting force and cutting temperature. To obtain the fine surface quality and avoid unnecessary loss, it is necessary to monitor the state of tool wear in the dry turning. In this article, the cutting force, vibration signal and surface texture of the machined surface were collected by tool condition monitoring system and signal processing techniques are being used for extracting the time-domain, frequency-domain and time-frequency features of cutting force and vibration. The gray level processing technique is used to extract the features of the gray co-occurrence matrix of the surface texture and found that these features changed simultaneously when the cutting tool broke. After this, an intelligent prediction model of tool wear was built using the support vector regression (SVR) whose kernel function parameters were optimized by the grid search algorithm (GS), the genetic algorithm (GA) and the particle swarm optimization algorithm respectively. The features extracted from the signals and surface texture are used to train the prediction model in MATLAB. It was found that after the surface texture features were fused using the intelligent prediction model on the basis of the features of cutting force and vibration, prediction accuracy of the proposed method is found as 97.32% and 96.72% respectively under the two prediction models of GA-SVR and GS-SVR. Moreover, the intelligent prediction model can not only predict the tool wear under different cutting conditions, but also the different wear stages in a single wear cycle and the absolute error between the predicted value and the actual value is less than 10 μm, the confidence coefficient of prediction curve is around 0.99.

scholarly journals Damage evolution in wood – pattern recognition based on acoustic emission (AE) frequency spectra

Holzforschung ◽  
2015 ◽  
Vol 69 (3) ◽  
pp. 357-365 ◽  
Author(s):  
Franziska Baensch ◽  
Markus G.R. Sause ◽  
Andreas J. Brunner ◽  
Peter Niemz
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Cluster Formation ◽  
Low Frequency ◽  
Tensile Tests ◽  
Most Probable Number ◽  
Frequency Spectra ◽  
Probable Number ◽  
Ae Signal ◽  
Ae Signals

Abstract Tensile tests on miniature spruce specimens have been performed by means of acoustic emission (AE) analysis. Stress was applied perpendicular (radial direction) and parallel to the grain. Nine features were selected from the AE frequency spectra. The signals were classified by means of an unsupervised pattern recognition approach, and natural classes of AE signals were identified based on the selected features. The algorithm calculates the numerically best partition based on subset combinations of the features provided for the analysis and leads to the most significant partition including the respective feature combination and the most probable number of clusters. For both specimen types investigated, the pattern recognition technique indicates two AE signal clusters. Cluster A comprises AE signals with a relatively high share of low-frequency components, and the opposite is true for cluster B. It is hypothesized that the signature of rapid and slow crack growths might be the origin for this cluster formation.

Sign in / Sign up

Export Citation Format

Share Document