Guided waves have been used for many years to find defects where there is no direct access to the area of interest. As the nondestructive testing method has grown in popularity, asset owners have increased their expectations and frequently request inspectors to quantify the severity of any damage detected. Recent developments in this field have prompted a renewed interest in the cutoff frequency sizing technique. In this technique, guided waves with different wavelengths are passed through a corroded area, and the thinner section acts as a low-pass filter that “cuts off” certain frequencies as the waves travel through it. By measuring the frequency content of the waves that pass through or are reflected by the damaged area, the remaining wall can be estimated. In this work, we provide an analysis of the limitations of this technique, which can lead to significant overestimation of the remaining wall depending on the shape of the defects. In the end, the authors propose a potential path forward in which conventional amplitude and frequency measurements are used to estimate the shape and depth of the defects, which can be used by themselves or in combination with cutoff frequency information to increase the validity and sizing accuracy for practical use.
{"title":"Limitations of the Cutoff Frequency technique for Sizing Defects with Guided Waves and a Potential Path Forward","authors":"Dileep Koodalil, Borja Lopez, Syed Ali, Alvaro Pallares","doi":"10.32548/2023.me-04338","DOIUrl":"https://doi.org/10.32548/2023.me-04338","url":null,"abstract":"Guided waves have been used for many years to find defects where there is no direct access to the area of interest. As the nondestructive testing method has grown in popularity, asset owners have increased their expectations and frequently request inspectors to quantify the severity of any damage detected. Recent developments in this field have prompted a renewed interest in the cutoff frequency sizing technique. In this technique, guided waves with different wavelengths are passed through a corroded area, and the thinner section acts as a low-pass filter that “cuts off” certain frequencies as the waves travel through it. By measuring the frequency content of the waves that pass through or are reflected by the damaged area, the remaining wall can be estimated. In this work, we provide an analysis of the limitations of this technique, which can lead to significant overestimation of the remaining wall depending on the shape of the defects. In the end, the authors propose a potential path forward in which conventional amplitude and frequency measurements are used to estimate the shape and depth of the defects, which can be used by themselves or in combination with cutoff frequency information to increase the validity and sizing accuracy for practical use.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135053949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.
{"title":"Real-Time AI driven Interpretation of Ultrasonic Data from Resistance Spot Weld Process Monitoring For Adaptive Welding","authors":"R. Scott, D. Stocco, A. Chertov, Roman Gr. Maev","doi":"10.32548/2023.me-04344","DOIUrl":"https://doi.org/10.32548/2023.me-04344","url":null,"abstract":"Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42058223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While most of the papers in this special issue explore the use of artificial intelligence and machine learning (AI/ML) to support the evaluation of nondestructive testing (NDT) data and assist with the classification of NDT indications, there are other important ways that emerging AI tools may impact how we work in NDT. The article discusses the recent emergence of AI chatbots, also referred to as generative artificial intelligence agents or large language models (LLMs), and highlights the potential benefits and risks as part of work in the NDT field.
{"title":"Benefits and Concerns of Using Emerging Artificial Intelligence Chatbots With Work in NDT","authors":"John Aldrin","doi":"10.32548/2023.me-04361","DOIUrl":"https://doi.org/10.32548/2023.me-04361","url":null,"abstract":"While most of the papers in this special issue explore the use of artificial intelligence and machine learning (AI/ML) to support the evaluation of nondestructive testing (NDT) data and assist with the classification of NDT indications, there are other important ways that emerging AI tools may impact how we work in NDT. The article discusses the recent emergence of AI chatbots, also referred to as generative artificial intelligence agents or large language models (LLMs), and highlights the potential benefits and risks as part of work in the NDT field.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42188180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The proliferation of machine learning (ML) advances will have long-lasting effects on the nondestructive testing/evaluation (NDT/E) community. As these advances impact the field and as new datasets are created to support these methods, it is important for researchers and practitioners to understand the associated challenges. This article provides basic definitions from the ML literature and tips for nondestructive researchers and practitioners to choose an ML architecture and to understand its relationships with the associated data. By the conclusion of this article, the reader will be able to identify the type of ML architecture needed for a given problem, be aware of how characteristics of the data affect the architecture’s training, and understand how to evaluate the ML performance based on properties of the dataset.
{"title":"Tips for Effective Machine Learning in NDT/E","authors":"J. Harley, S. Zafar, Charlie Tran","doi":"10.32548/2023.me-04358","DOIUrl":"https://doi.org/10.32548/2023.me-04358","url":null,"abstract":"The proliferation of machine learning (ML) advances will have long-lasting effects on the nondestructive testing/evaluation (NDT/E) community. As these advances impact the field and as new datasets are created to support these methods, it is important for researchers and practitioners to understand the associated challenges. This article provides basic definitions from the ML literature and tips for nondestructive researchers and practitioners to choose an ML architecture and to understand its relationships with the associated data. By the conclusion of this article, the reader will be able to identify the type of ML architecture needed for a given problem, be aware of how characteristics of the data affect the architecture’s training, and understand how to evaluate the ML performance based on properties of the dataset.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42730447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuhui Huang, Obaid Elshafiey, Karim Farzia, L. Udpa, Ming Han, Y. Deng
This paper presents a novel data-driven approach to localize two types of acoustic emission sources in an aluminum plate, namely a Hsu-Nielsen source, which simulates a crack-like source, and steel ball impacts of varying diameters acting as the impact source. While deep neural networks have shown promise in previous studies, achieving high accuracy requires a large amount of training data, which may not always be feasible. To address this challenge, we investigated the applicability of transfer learning to address the issue of limited training data. Our approach involves transferring knowledge learned from numerical modeling to the experimental domain to localize nine different source locations. In the process, we evaluated six deep learning architectures using tenfold cross-validation and demonstrated the potential of transfer learning for efficient acoustic emission source localization, even with limited experimental data. This study contributes to the growing demand for running deep learning models with limited capacity and training time and highlights the promise of transfer learning methods such as fine-tuning pretrained models on large semi-related datasets.
{"title":"Acoustic Emission Source Localization using Deep Transfer Learning and Finite Element Modeling–based Knowledge Transfer","authors":"Xuhui Huang, Obaid Elshafiey, Karim Farzia, L. Udpa, Ming Han, Y. Deng","doi":"10.32548/2023.me-04348","DOIUrl":"https://doi.org/10.32548/2023.me-04348","url":null,"abstract":"This paper presents a novel data-driven approach to localize two types of acoustic emission sources in an aluminum plate, namely a Hsu-Nielsen source, which simulates a crack-like source, and steel ball impacts of varying diameters acting as the impact source. While deep neural networks have shown promise in previous studies, achieving high accuracy requires a large amount of training data, which may not always be feasible. To address this challenge, we investigated the applicability of transfer learning to address the issue of limited training data. Our approach involves transferring knowledge learned from numerical modeling to the experimental domain to localize nine different source locations. In the process, we evaluated six deep learning architectures using tenfold cross-validation and demonstrated the potential of transfer learning for efficient acoustic emission source localization, even with limited experimental data. This study contributes to the growing demand for running deep learning models with limited capacity and training time and highlights the promise of transfer learning methods such as fine-tuning pretrained models on large semi-related datasets.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49172110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While artificial intelligence/machine learning (AI/ML) methods have shown promise for the analysis of image and signal data, applications using nondestructive testing (NDT) for managing the safety of systems must meet a high level of quantified capability. Engineering decisions require technique validation with statistical bounds on performance to enable integration into critical analyses, such as life management and risk analysis. The Air Force Research Laboratory (AFRL) has pursued several projects to apply a hybrid approach that integrates AI/ML methods with heuristic and model-based algorithms to assist inspectors in accomplishing complex NDT evaluations. Three such examples are described in this article, including a method that was validated through a probability of detection (POD) study and deployed by the Department of the Air Force (DAF) in 2004 (Lindgren et al. 2005). Key lessons learned include the importance of considering the wide variability present in NDT applications upfront and maintaining a critical role for human inspectors to ensure NDT data quality and address outlier indications.
{"title":"Validated and Deployable AI/ML for NDT Data Diagnostics","authors":"E. Lindgren","doi":"10.32548/2023.me-04364","DOIUrl":"https://doi.org/10.32548/2023.me-04364","url":null,"abstract":"While artificial intelligence/machine learning (AI/ML) methods have shown promise for the analysis of image and signal data, applications using nondestructive testing (NDT) for managing the safety of systems must meet a high level of quantified capability. Engineering decisions require technique validation with statistical bounds on performance to enable integration into critical analyses, such as life management and risk analysis. The Air Force Research Laboratory (AFRL) has pursued several projects to apply a hybrid approach that integrates AI/ML methods with heuristic and model-based algorithms to assist inspectors in accomplishing complex NDT evaluations. Three such examples are described in this article, including a method that was validated through a probability of detection (POD) study and deployed by the Department of the Air Force (DAF) in 2004 (Lindgren et al. 2005). Key lessons learned include the importance of considering the wide variability present in NDT applications upfront and maintaining a critical role for human inspectors to ensure NDT data quality and address outlier indications.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44585397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There have been numerous efforts in the metrology, manufacturing, and nondestructive evaluation communities to investigate various methods for effective in situ monitoring of additive manufacturing processes. Researchers have investigated the use of a variety of techniques and sensors and found that each has its own unique capabilities as well as limitations. Among all measurement techniques, acoustic-based in situ measurements of additive manufacturing processes provide remarkable data and advantages for process and part quality assessment. Acoustic signals contain crucial information about the manufacturing processes and fabricated components with a sufficient sampling rate. Like any other measurement technique, acoustic-based methods have specific challenges regarding applications and data interpretation. The enormous size and complexity of the data structure are significant challenges when dealing with acoustic data for in situ process monitoring. To address this issue, researchers have explored and investigated various data and signal processing techniques empowered by artificial intelligence and machine learning methods to extract practical information from acoustic signals. This paper aims to survey recent and innovative machine learning techniques and approaches for acoustic data processing in additive manufacturing in situ monitoring.
{"title":"Machine Learning Techniques for Acoustic Data Processing in Additive Manufacturing In Situ Process Monitoring: A Review","authors":"H. Taheri, S. Zafar","doi":"10.32548/2023.me-04356","DOIUrl":"https://doi.org/10.32548/2023.me-04356","url":null,"abstract":"There have been numerous efforts in the metrology, manufacturing, and nondestructive evaluation communities to investigate various methods for effective in situ monitoring of additive manufacturing processes. Researchers have investigated the use of a variety of techniques and sensors and found that each has its own unique capabilities as well as limitations. Among all measurement techniques, acoustic-based in situ measurements of additive manufacturing processes provide remarkable data and advantages for process and part quality assessment. Acoustic signals contain crucial information about the manufacturing processes and fabricated components with a sufficient sampling rate. Like any other measurement technique, acoustic-based methods have specific challenges regarding applications and data interpretation. The enormous size and complexity of the data structure are significant challenges when dealing with acoustic data for in situ process monitoring. To address this issue, researchers have explored and investigated various data and signal processing techniques empowered by artificial intelligence and machine learning methods to extract practical information from acoustic signals. This paper aims to survey recent and innovative machine learning techniques and approaches for acoustic data processing in additive manufacturing in situ monitoring.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49203924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Jia, Pengchao Chen, Rui Li, Kuan-Chung Fu, Rongbiao Wang, K. Song
Detecting inner- and outer-surface discontinuities of drill pipe is of great significance to the evaluation of the quality of the drill pipe. This paper proposes a method based on a magnetized eddy current testing technique to detect inner- and outer-surface discontinuities by analyzing the difference of the imaginary part signal characteristics of the receiving coil. For eddy current testing, the outer-surface discontinuities cause the local conductivity to be zero, while inner-surface discontinuities cause the perturbation of the magnetic permeability on the material surface. In this paper, the effects of conductivity distortion and permeability perturbation on induced eddy currents are analyzed by simulation. The conductivity distortion increases the magnetic field above the discontinuity compared to the magnetic field without the discontinuity, while the permeability perturbation reduces the magnetic field. Next, the difference in coil impedance can be used to distinguish the inner- and outer-surface discontinuities. Finally, the feasibility of the method is verified by experiments, and the results show that the inner- and outer-surface discontinuities can be discriminated.
{"title":"A Novel Method for Distinguishing Discontinuities In Ferromagnetic Materials Based On Eddy Current Testing Under Magnetization","authors":"G. Jia, Pengchao Chen, Rui Li, Kuan-Chung Fu, Rongbiao Wang, K. Song","doi":"10.32548/2023.me-04284","DOIUrl":"https://doi.org/10.32548/2023.me-04284","url":null,"abstract":"Detecting inner- and outer-surface discontinuities of drill pipe is of great significance to the evaluation of the quality of the drill pipe. This paper proposes a method based on a magnetized eddy current testing technique to detect inner- and outer-surface discontinuities by analyzing the difference of the imaginary part signal characteristics of the receiving coil. For eddy current testing, the outer-surface discontinuities cause the local conductivity to be zero, while inner-surface discontinuities cause the perturbation of the magnetic permeability on the material surface. In this paper, the effects of conductivity distortion and permeability perturbation on induced eddy currents are analyzed by simulation. The conductivity distortion increases the magnetic field above the discontinuity compared to the magnetic field without the discontinuity, while the permeability perturbation reduces the magnetic field. Next, the difference in coil impedance can be used to distinguish the inner- and outer-surface discontinuities. Finally, the feasibility of the method is verified by experiments, and the results show that the inner- and outer-surface discontinuities can be discriminated.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41971673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weld inspection of stainless steel pipes and pressure vessels is one of the most challenging and difficult inspections for ultrasonic testing. This is due to variations in grain structure and associated anisotropy. Anisotropy causes grain scattering and adversely affects propagation of sound waves. The effect is more telling for shear waves, which, in many cases, have almost no ability to penetrate the weld volume. Longitudinal waves are affected to a lesser degree by anisotropy and can be applied for such tests. Angle beam or refracted longitudinal waves are, therefore, the accepted method for stainless steel weld inspections.
{"title":"Phased Array Ultrasonic Testing of Stainless Steel Pipe Welds","authors":"A. Birring, James Williams","doi":"10.32548/2023.me-04333","DOIUrl":"https://doi.org/10.32548/2023.me-04333","url":null,"abstract":"Weld inspection of stainless steel pipes and pressure vessels is one of the most challenging and difficult inspections for ultrasonic testing. This is due to variations in grain structure and associated anisotropy. Anisotropy causes grain scattering and adversely affects propagation of sound waves. The effect is more telling for shear waves, which, in many cases, have almost no ability to penetrate the weld volume. Longitudinal waves are affected to a lesser degree by anisotropy and can be applied for such tests. Angle beam or refracted longitudinal waves are, therefore, the accepted method for stainless steel weld inspections.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42197188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the effects of emery abraded surface roughness orientation on the shear strength of the carbon fiber reinforced polymer (CFRP) single lap joint (SLJ). For this purpose, three roughness patterns of angles 0°, 45°, and 90° with the longitudinal axis of adherend were considered in the overlap area of the SLJ. The surface roughness was characterized by contact-based roughness measurement and contact angle between the water droplet and the adherend surface. Through-the-thickness full-strain field measurement was carried out during shear strength tests using digital image correlation (DIC). The peel and shear stress at the overlap end were highest in the 90° coupons and least in 0° coupons. Acoustic emission testing (AE) was carried out during the shear strength testing of the SLJ. The investigation proves that the surface roughness orientation at the interface of bonded joints affects the acoustic emissions generated. AE hits and amplitude parameter distribution was found to change with the change in orientation. AE hits were more in 90° samples and least in 0° samples.
{"title":"Evaluation of Influence of Surface Roughness Orientation in CFRP Lap Joints using AE and DIC","authors":"L. S. Mane, M. Bhat","doi":"10.32548/2023.me-04326","DOIUrl":"https://doi.org/10.32548/2023.me-04326","url":null,"abstract":"This paper investigates the effects of emery abraded surface roughness orientation on the shear strength of the carbon fiber reinforced polymer (CFRP) single lap joint (SLJ). For this purpose, three roughness patterns of angles 0°, 45°, and 90° with the longitudinal axis of adherend were considered in the overlap area of the SLJ. The surface roughness was characterized by contact-based roughness measurement and contact angle between the water droplet and the adherend surface. Through-the-thickness full-strain field measurement was carried out during shear strength tests using digital image correlation (DIC). The peel and shear stress at the overlap end were highest in the 90° coupons and least in 0° coupons. Acoustic emission testing (AE) was carried out during the shear strength testing of the SLJ. The investigation proves that the surface roughness orientation at the interface of bonded joints affects the acoustic emissions generated. AE hits and amplitude parameter distribution was found to change with the change in orientation. AE hits were more in 90° samples and least in 0° samples.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47025774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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