Guangdong Zhang, Xiongbing Li, Tianji Li, T. Kundu
Ductile metallic materials such as aluminum alloy, brass and steel are widely used in engineering structures. Monitoring elastoplastic deformation in these materials is important for structural health monitoring (SHM) for ensuring the safety of structures made of metallic materials. This paper presents a newly developed and promising nonlinear ultrasonic (NLU) technique called Sideband Peak Count - Index (or SPC-I) for monitoring the early stages of elastoplastic deformation in ductile metallic alloy Al6061. Experimental results presented in this paper shows that in the elastic range of the Al6061 SPC-I values show slight changes may be due to the inherent inhomogeneities (imperfect grain boundaries or dislocations at the grain boundaries under loadings) of Al6061. Then the SPC-I value changes rapidly as the material enters the plastic range zone. Compared to the linear ultrasonic (LU) parameters (wave velocity and attenuation changes) the SPC-I shows noticeable advantage (higher sensitivity) for monitoring the early stages of the elastoplastic deformation in these ductile metallic specimens investigated in this study. It is concluded that SPC-I technique is useful for monitoring deformations in ductile metallic materials, especially in their plastic zone. This work extends the applicability of the SPC-I technique for monitoring elastoplastic deformations in metallic specimens that has not been reported in earlier works and can provide some guidelines for SHM related to elastoplastic deformation in metallic structures.
{"title":"Monitoring elastoplastic deformation in ductile metallic materials using sideband peak count - index (SPC-I) technique","authors":"Guangdong Zhang, Xiongbing Li, Tianji Li, T. Kundu","doi":"10.1115/1.4062930","DOIUrl":"https://doi.org/10.1115/1.4062930","url":null,"abstract":"\u0000 Ductile metallic materials such as aluminum alloy, brass and steel are widely used in engineering structures. Monitoring elastoplastic deformation in these materials is important for structural health monitoring (SHM) for ensuring the safety of structures made of metallic materials. This paper presents a newly developed and promising nonlinear ultrasonic (NLU) technique called Sideband Peak Count - Index (or SPC-I) for monitoring the early stages of elastoplastic deformation in ductile metallic alloy Al6061. Experimental results presented in this paper shows that in the elastic range of the Al6061 SPC-I values show slight changes may be due to the inherent inhomogeneities (imperfect grain boundaries or dislocations at the grain boundaries under loadings) of Al6061. Then the SPC-I value changes rapidly as the material enters the plastic range zone. Compared to the linear ultrasonic (LU) parameters (wave velocity and attenuation changes) the SPC-I shows noticeable advantage (higher sensitivity) for monitoring the early stages of the elastoplastic deformation in these ductile metallic specimens investigated in this study. It is concluded that SPC-I technique is useful for monitoring deformations in ductile metallic materials, especially in their plastic zone. This work extends the applicability of the SPC-I technique for monitoring elastoplastic deformations in metallic specimens that has not been reported in earlier works and can provide some guidelines for SHM related to elastoplastic deformation in metallic structures.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"22 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77548051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. G. Salunkhe, S. Khot, R. Desavale, Nitesh P. Yelve
The dynamic characteristics of rolling element bearings are strongly related to their geometric and operating parameters, most importantly the bearing unbalance. Modern condition monitoring necessitates the use of intrinsic mode functions (IMFs) to diagnose unbalance bearing failure. This paper presents an Hilbert–Huang transform (HHT) method to diagnose the unbalanced rolling bearing faults of rotating machinery. To initially reduce the noise levels with slight signal distortion, the noises of the sample in normal and unbalanced fault states are measured and denoised using the wavelet threshold approach. The complex vibration signatures are decomposed into finite IMFs with ensemble empirical mode decomposition technique. Fast Fourier techniques (FFT) are employed to extract the vibration responses of bearings that are artificially damaged using electrochemical machining on a newly established test setup for rotor disc bearings. The similarities between the information-contained marginal Hilbert spectra can be used to diagnose rotating machinery bearing faults. The data marginal Hilbert spectra of Mahalanobis and cosine index are compared to determine the fault indicator index's similarity score. The HHT models simplicity enhanced the precision of diagnosis correlated to the results of the experiments with weak fault characteristic signals. The effectiveness of the proposed approach is evaluated with several theoretical models from the literature. The HHT approach is experimentally proven with unbalance diagnosis and capable of classifying marginal Hilbert spectra distribution. Because of its superior time-frequency characteristics and pattern identification of marginal Hilbert spectra and fault indicator indices, the newly stated HHT can process nonlinear, non-stationary, and even transient signals. The findings demonstrate that the suggested method is superior in terms of unbalance fault identification accuracy for monitoring the dynamic stability of industrial rotating machinery.
{"title":"Unbalance Bearing Fault Identification Using Highly Accurate Hilbert-Huang Transform Approach","authors":"V. G. Salunkhe, S. Khot, R. Desavale, Nitesh P. Yelve","doi":"10.1115/1.4062929","DOIUrl":"https://doi.org/10.1115/1.4062929","url":null,"abstract":"\u0000 The dynamic characteristics of rolling element bearings are strongly related to their geometric and operating parameters, most importantly the bearing unbalance. Modern condition monitoring necessitates the use of intrinsic mode functions (IMFs) to diagnose unbalance bearing failure. This paper presents an Hilbert–Huang transform (HHT) method to diagnose the unbalanced rolling bearing faults of rotating machinery. To initially reduce the noise levels with slight signal distortion, the noises of the sample in normal and unbalanced fault states are measured and denoised using the wavelet threshold approach. The complex vibration signatures are decomposed into finite IMFs with ensemble empirical mode decomposition technique. Fast Fourier techniques (FFT) are employed to extract the vibration responses of bearings that are artificially damaged using electrochemical machining on a newly established test setup for rotor disc bearings. The similarities between the information-contained marginal Hilbert spectra can be used to diagnose rotating machinery bearing faults. The data marginal Hilbert spectra of Mahalanobis and cosine index are compared to determine the fault indicator index's similarity score. The HHT models simplicity enhanced the precision of diagnosis correlated to the results of the experiments with weak fault characteristic signals. The effectiveness of the proposed approach is evaluated with several theoretical models from the literature. The HHT approach is experimentally proven with unbalance diagnosis and capable of classifying marginal Hilbert spectra distribution. Because of its superior time-frequency characteristics and pattern identification of marginal Hilbert spectra and fault indicator indices, the newly stated HHT can process nonlinear, non-stationary, and even transient signals. The findings demonstrate that the suggested method is superior in terms of unbalance fault identification accuracy for monitoring the dynamic stability of industrial rotating machinery.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"98 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81072705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Nonlinear Lamb waves including second harmonic and acoustic-radiation-induced quasi-static components (QSC) have a potential for accurately evaluating early-stage fatigue damage. Most previous studies focus on second-harmonic-based techniques that require phase velocity matching and are hard to isolate interferences from ultrasonic testing systems. The aforementioned requirement and deficiency limit applications of the second-harmonic-based techniques. In this study, a QSC-based technique of low-frequency Lamb waves is proposed for early-stage fatigue damage evaluation of metal plates, which does not need to require phase velocity matching and can remove interferences from ultrasonic testing systems. Both in simulations and in experiments, the primary Lamb wave mode at a low frequency that meets approximate group velocity matching with the generated QSC is selected. In finite element simulations, different levels of material nonlinearities by changing the third-order elastic constants are used to characterize levels of fatigue damage. Numerical results show that the magnitude of the generated QSC pulse increases with the levels of fatigue damage. Early-stage fatigue damage in aluminum plates with different fatigue cycles is further experimentally evaluated. The generated QSC pulse is extracted from received time-domain signals using the phase-inversion technique and low-pass digital filtering processing. The curve of the normalized relative acoustic nonlinearity parameter versus the cyclic loading number is obtained. Numerical simulations and experimental results show that the early-stage fatigue damage in aluminum plates can effectively be evaluated using the QSC generated by low-frequency Lamb waves.
{"title":"Evaluation of Early-Stage Fatigue Damage in Metal Plates Using Quasi-Static Components of Low-Frequency Lamb Waves","authors":"Kun Wu, Caibin Xu, Mingxi Deng","doi":"10.1115/1.4062651","DOIUrl":"https://doi.org/10.1115/1.4062651","url":null,"abstract":"Abstract Nonlinear Lamb waves including second harmonic and acoustic-radiation-induced quasi-static components (QSC) have a potential for accurately evaluating early-stage fatigue damage. Most previous studies focus on second-harmonic-based techniques that require phase velocity matching and are hard to isolate interferences from ultrasonic testing systems. The aforementioned requirement and deficiency limit applications of the second-harmonic-based techniques. In this study, a QSC-based technique of low-frequency Lamb waves is proposed for early-stage fatigue damage evaluation of metal plates, which does not need to require phase velocity matching and can remove interferences from ultrasonic testing systems. Both in simulations and in experiments, the primary Lamb wave mode at a low frequency that meets approximate group velocity matching with the generated QSC is selected. In finite element simulations, different levels of material nonlinearities by changing the third-order elastic constants are used to characterize levels of fatigue damage. Numerical results show that the magnitude of the generated QSC pulse increases with the levels of fatigue damage. Early-stage fatigue damage in aluminum plates with different fatigue cycles is further experimentally evaluated. The generated QSC pulse is extracted from received time-domain signals using the phase-inversion technique and low-pass digital filtering processing. The curve of the normalized relative acoustic nonlinearity parameter versus the cyclic loading number is obtained. Numerical simulations and experimental results show that the early-stage fatigue damage in aluminum plates can effectively be evaluated using the QSC generated by low-frequency Lamb waves.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Ekwaro-Osire, Nazir Laureano Gandur, Camilo Alberto Lopez-Salazar
If the incipient fault (IF) point is not corrected, it may imply a poor prognostic framework, a false value of remaining useful life (RUL), and unexpected catastrophic failure. The use of the concept of multiscale diversity entropy (MDE) in the context of predicting IF is a novel area that has yet to be fully explored. Since MDE is commonly used for measuring a system's dynamic complexity from a signal, it is worth exploring for predicting IF. Can MDE be used to develop a framework to predict the IF of a system? This study developed a new framework to determine the IF. The performance of the framework was demonstrated on bearing data and battery data. Additionally, the results of this study were compared with another methodology, widely used for predicting IF. In conclusion, this new methodology produces a more accurate prediction of IF because of the physical principles associated with MDE.
{"title":"Incipient Fault Point Detection Based on Multiscale Diversity Entropy","authors":"S. Ekwaro-Osire, Nazir Laureano Gandur, Camilo Alberto Lopez-Salazar","doi":"10.1115/1.4062622","DOIUrl":"https://doi.org/10.1115/1.4062622","url":null,"abstract":"\u0000 If the incipient fault (IF) point is not corrected, it may imply a poor prognostic framework, a false value of remaining useful life (RUL), and unexpected catastrophic failure. The use of the concept of multiscale diversity entropy (MDE) in the context of predicting IF is a novel area that has yet to be fully explored. Since MDE is commonly used for measuring a system's dynamic complexity from a signal, it is worth exploring for predicting IF. Can MDE be used to develop a framework to predict the IF of a system? This study developed a new framework to determine the IF. The performance of the framework was demonstrated on bearing data and battery data. Additionally, the results of this study were compared with another methodology, widely used for predicting IF. In conclusion, this new methodology produces a more accurate prediction of IF because of the physical principles associated with MDE.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"87 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77678655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suyog Jadhav, Ravali Kuchibhotla, Krishna Agarwal, A. Habib, Dilip K. Prasad
The versatile nature of ultrasound imaging finds applications in various fields. A point contact excitation and detection method is generally used for visualizing the acoustic waves in Lead Zirconate Titanate (PZT) ceramics. Such an excitation method with a delta pulse generates a broadband frequency spectrum and wide directional wave vector. The presence of noise in the ultrasonic signals severely degrades the resolution and image quality. Deep learning-based signal and image denoising has been demonstrated recently. This paper bench-marked and compared several state-of-the-art deep learning image denoising methods with the classical denoising methods. The best-performing deep learning models are observed to be performing at par or, in some cases, even better than the classical methods on ultrasonic images. We further demonstrate the effectiveness and versatility of the deep learning-based denoising model for the unexplored domain of ultrasound/ultrasonic data. We conclude with a discussion on selecting the best method for denoising ultrasonic images. The impact of this work may help ultrasound-based defects identification equipment manufacturers to adopt a deep learning-based denoising model for more wider and versatile use.
{"title":"Deep learning-based denoising of acoustic images generated with point contact method","authors":"Suyog Jadhav, Ravali Kuchibhotla, Krishna Agarwal, A. Habib, Dilip K. Prasad","doi":"10.1115/1.4062515","DOIUrl":"https://doi.org/10.1115/1.4062515","url":null,"abstract":"\u0000 The versatile nature of ultrasound imaging finds applications in various fields. A point contact excitation and detection method is generally used for visualizing the acoustic waves in Lead Zirconate Titanate (PZT) ceramics. Such an excitation method with a delta pulse generates a broadband frequency spectrum and wide directional wave vector. The presence of noise in the ultrasonic signals severely degrades the resolution and image quality. Deep learning-based signal and image denoising has been demonstrated recently. This paper bench-marked and compared several state-of-the-art deep learning image denoising methods with the classical denoising methods. The best-performing deep learning models are observed to be performing at par or, in some cases, even better than the classical methods on ultrasonic images. We further demonstrate the effectiveness and versatility of the deep learning-based denoising model for the unexplored domain of ultrasound/ultrasonic data. We conclude with a discussion on selecting the best method for denoising ultrasonic images. The impact of this work may help ultrasound-based defects identification equipment manufacturers to adopt a deep learning-based denoising model for more wider and versatile use.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"21 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78146464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advances in sensing and nondestructive evaluation methods have increased the interest in developing data-driven modeling and associated computational workflows for model-updating, in relation also to a variety of emerging digital twin applications. In this context, of particular interest in this investigation are transient effects that lead to or are caused by deformation instabilities, typically found in the cases of complex material behavior or in interactions between material and geometry. In both cases, deformation localizations are observed which are typically also related to damage effects. This manuscript describes a novel framework to incorporate deformation data into a finite element model (FEM) that has been formulated using non-local mechanics and is capable of receiving such data and using it to describe the development of localizations. Specifically, experimentally measured full field displacement data is used as an input in FEM as an ad-hoc boundary condition at any or every node in the body. To achieve this goal, a plasticity model which includes a spatially averaged non-local hardening parameter in the yield criterion is used to account for associated numerical instabilities and mesh dependence. Furthermore, the introduction of a length scale parameter into the constitutive law allows the connection between material behavior, geometry and localizations. Additional steps remove the experimental data and evolve the computational predictions forward in time. Both one and three-dimensional boundary value problems are used to present results obtained by the proposed framework, while comments are made in terms of its potential uses.
{"title":"Leveraging Full Field Deformation Measurements in Computational Modeling of Damage","authors":"Sara Schlenker, E. Tekerek, A. Kontsos","doi":"10.1115/1.4062291","DOIUrl":"https://doi.org/10.1115/1.4062291","url":null,"abstract":"\u0000 Advances in sensing and nondestructive evaluation methods have increased the interest in developing data-driven modeling and associated computational workflows for model-updating, in relation also to a variety of emerging digital twin applications. In this context, of particular interest in this investigation are transient effects that lead to or are caused by deformation instabilities, typically found in the cases of complex material behavior or in interactions between material and geometry. In both cases, deformation localizations are observed which are typically also related to damage effects. This manuscript describes a novel framework to incorporate deformation data into a finite element model (FEM) that has been formulated using non-local mechanics and is capable of receiving such data and using it to describe the development of localizations. Specifically, experimentally measured full field displacement data is used as an input in FEM as an ad-hoc boundary condition at any or every node in the body. To achieve this goal, a plasticity model which includes a spatially averaged non-local hardening parameter in the yield criterion is used to account for associated numerical instabilities and mesh dependence. Furthermore, the introduction of a length scale parameter into the constitutive law allows the connection between material behavior, geometry and localizations. Additional steps remove the experimental data and evolve the computational predictions forward in time. Both one and three-dimensional boundary value problems are used to present results obtained by the proposed framework, while comments are made in terms of its potential uses.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"32 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81338770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, vibration-based fault diagnostics and response classification have been done for defective high-speed cylindrical bearing operating under unbalance rotor conditions. An experimental study has been performed to capture the vibration signature of faulty bearings in the time domain and for different speeds of the unbalanced rotor. Two-dimensional phase trajectories are generated by estimating the time delay and embedding dimension corresponding to vibration signatures. Qualitative analysis involves the implementation of a Deep Convolutional Neural Network (DCNN) utilizing the phase portraits as input to classify the nonlinear vibration responses. Comparison with state-of-art classifiers such as ANN, DNN, and KNN is presented based on classification accuracy values. Thus, the values obtained are 61.12%, 66.62%, 71.85%, and 98.85% for ANN, DNN, KNN, and DCNN, respectively. Hence, the proposed intelligent classification model accurately identifies the dynamic behavior of bearing under unbalanced rotor conditions.
{"title":"Fault Diagnostics and Faulty Pattern Analysis of High-Speed Roller Bearings Using Deep Convolutional Neural network","authors":"M. Rathore, S. Harsha","doi":"10.1115/1.4062252","DOIUrl":"https://doi.org/10.1115/1.4062252","url":null,"abstract":"\u0000 In this paper, vibration-based fault diagnostics and response classification have been done for defective high-speed cylindrical bearing operating under unbalance rotor conditions. An experimental study has been performed to capture the vibration signature of faulty bearings in the time domain and for different speeds of the unbalanced rotor. Two-dimensional phase trajectories are generated by estimating the time delay and embedding dimension corresponding to vibration signatures. Qualitative analysis involves the implementation of a Deep Convolutional Neural Network (DCNN) utilizing the phase portraits as input to classify the nonlinear vibration responses. Comparison with state-of-art classifiers such as ANN, DNN, and KNN is presented based on classification accuracy values. Thus, the values obtained are 61.12%, 66.62%, 71.85%, and 98.85% for ANN, DNN, KNN, and DCNN, respectively. Hence, the proposed intelligent classification model accurately identifies the dynamic behavior of bearing under unbalanced rotor conditions.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"47 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84824070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a contactless nonlinear acoustic method is developed for the evaluation of stress states in anisotropic metallic plates by combination of electromagnetic acoustic resonance (EMAR) technique and higher harmonic generation. Electromagnetic transducers (EMATs) designed and applied for exciting and receiving ultrasonic signals can maintain the coupling condition consistently on the measure of higher harmonics generated. EMAR provides sufficient magnitude of signals for higher harmonics generated. In addition, conventional EMAR technique based on the measurement of shear wave velocity and attenuation within a certain frequency range, is also carried out in the specimens. Effect of stress on the higher harmonic generation is explored and discussed. It is found that nonlinear parameters measured by nonlinear EMAR method change significantly versus the increase of external tension stress loadings, whereas the variations of linear acoustic parameters measured are negligible. In addition, the obtained results clearly indicate that the variation of the measured acoustic nonlinear parameters versus external stresses is direction-depended in anisotropic materials. The contactless nonlinear acoustic technique combines the feature of EMAR with the merit of higher harmonic generation, providing an effective means for stress evaluation in weakly anisotropic materials with improved reliability and sensitivity over linear ones.
{"title":"Contactless evaluation of stress in anisotropic metallic plates using nonlinear electromagnetic acoustic resonance technique","authors":"Weibin Li, Yi Hu, Tianze Shi, M. Deng","doi":"10.1115/1.4062253","DOIUrl":"https://doi.org/10.1115/1.4062253","url":null,"abstract":"\u0000 In this paper, a contactless nonlinear acoustic method is developed for the evaluation of stress states in anisotropic metallic plates by combination of electromagnetic acoustic resonance (EMAR) technique and higher harmonic generation. Electromagnetic transducers (EMATs) designed and applied for exciting and receiving ultrasonic signals can maintain the coupling condition consistently on the measure of higher harmonics generated. EMAR provides sufficient magnitude of signals for higher harmonics generated. In addition, conventional EMAR technique based on the measurement of shear wave velocity and attenuation within a certain frequency range, is also carried out in the specimens. Effect of stress on the higher harmonic generation is explored and discussed. It is found that nonlinear parameters measured by nonlinear EMAR method change significantly versus the increase of external tension stress loadings, whereas the variations of linear acoustic parameters measured are negligible. In addition, the obtained results clearly indicate that the variation of the measured acoustic nonlinear parameters versus external stresses is direction-depended in anisotropic materials. The contactless nonlinear acoustic technique combines the feature of EMAR with the merit of higher harmonic generation, providing an effective means for stress evaluation in weakly anisotropic materials with improved reliability and sensitivity over linear ones.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"14 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81913514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Small Punch Test (SPT) approach is a miniature specimen testing technique to estimate the preserved mechanical strength of an in-service component to check its fitness for service. The SPT results are summarised in form of force-specimen deflection, (F-u) and force-punch displacement, (F-v) response. There are many standards published in an attempt to define a universally accepted approach for SPT-aided echanical characterization. However, it was recognized that such standards were not concerned to practice a consistent approach while SPT response measurement and strength estimation towards outlining pro-claimed best-fitting correlations. This paper narrates limitations caused by known inconsistent practices and proposed comprehensive correlations for accurate strength estimation for metallic materials which are exposed to 100-2000 MPa strengths.
{"title":"Comprehensive correlations for small punch test response post-processing toward preserved mechanical strength estimation","authors":"Pruthvish Patel, BK Patel","doi":"10.1115/1.4062093","DOIUrl":"https://doi.org/10.1115/1.4062093","url":null,"abstract":"\u0000 The Small Punch Test (SPT) approach is a miniature specimen testing technique to estimate the preserved mechanical strength of an in-service component to check its fitness for service. The SPT results are summarised in form of force-specimen deflection, (F-u) and force-punch displacement, (F-v) response. There are many standards published in an attempt to define a universally accepted approach for SPT-aided echanical characterization. However, it was recognized that such standards were not concerned to practice a consistent approach while SPT response measurement and strength estimation towards outlining pro-claimed best-fitting correlations. This paper narrates limitations caused by known inconsistent practices and proposed comprehensive correlations for accurate strength estimation for metallic materials which are exposed to 100-2000 MPa strengths.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"56 4","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72559621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Kleman, Shoaib Anwar, Zhengchun Liu, Jiaqi Gong, Xishi Zhu, Austin Yunker, R. Kettimuthu, Jiaze He
Ultrasound computed tomography (USCT) shows great promise in nondestructive evaluation and medical imaging due to its ability to quickly scan and collect data from a region of interest. However, the processing of the collected data into a meaningful image requires both time and computational resources; existing approaches are a trade-off between the accuracy of the prediction and the speed at which the data can be analyzed. We propose to develop convolutional neural networks(CNNs) to accelerate and enhance the inversion results to reveal underlying structures or abnormalities that may be located within the region of interest. For training, the ultrasonic signals were first processed using the FWI technique for only a single iteration; the resulting image and the corresponding true model were used as the input and output, respectively. The proposed machine learning approach is based on implementing two-dimensional CNNs to find an approximate solution to the inverse problem of partial differential equation-based model reconstruction. To alleviate the time-consuming and computationally intensive data generation process, a high-performance computing (HPC)-based framework has been developed to generate the training data in parallel. At the inference stage, the acquired signals will be first processed by FWI for a single iteration; then the resulting image will be processed by a pre-trained CNN to instantaneously generate the final output image. The results showed that once trained, the CNN scan quickly generate the predicted wave speed distributions with significantly enhanced speed and accuracy.
{"title":"Full Waveform Inversion-Based Ultrasound Computed Tomography Acceleration Using 2D Convolutional Neural Networks","authors":"Christopher Kleman, Shoaib Anwar, Zhengchun Liu, Jiaqi Gong, Xishi Zhu, Austin Yunker, R. Kettimuthu, Jiaze He","doi":"10.1115/1.4062092","DOIUrl":"https://doi.org/10.1115/1.4062092","url":null,"abstract":"\u0000 Ultrasound computed tomography (USCT) shows great promise in nondestructive evaluation and medical imaging due to its ability to quickly scan and collect data from a region of interest. However, the processing of the collected data into a meaningful image requires both time and computational resources; existing approaches are a trade-off between the accuracy of the prediction and the speed at which the data can be analyzed. We propose to develop convolutional neural networks(CNNs) to accelerate and enhance the inversion results to reveal underlying structures or abnormalities that may be located within the region of interest. For training, the ultrasonic signals were first processed using the FWI technique for only a single iteration; the resulting image and the corresponding true model were used as the input and output, respectively. The proposed machine learning approach is based on implementing two-dimensional CNNs to find an approximate solution to the inverse problem of partial differential equation-based model reconstruction. To alleviate the time-consuming and computationally intensive data generation process, a high-performance computing (HPC)-based framework has been developed to generate the training data in parallel. At the inference stage, the acquired signals will be first processed by FWI for a single iteration; then the resulting image will be processed by a pre-trained CNN to instantaneously generate the final output image. The results showed that once trained, the CNN scan quickly generate the predicted wave speed distributions with significantly enhanced speed and accuracy.","PeriodicalId":52294,"journal":{"name":"Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems","volume":"13 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89837899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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