Materials Evaluation最新文献_第2页

Limitations of the Cutoff Frequency technique for Sizing Defects with Guided Waves and a Potential Path Forward 用导波和电位正向路径确定缺陷尺寸的截止频率技术的局限性

4区材料科学

Materials Evaluation Pub Date : 2023-08-01 DOI: 10.32548/2023.me-04338

Dileep Koodalil, Borja Lopez, Syed Ali, Alvaro Pallares

{"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}

引用次数: 0

Real-Time AI driven Interpretation of Ultrasonic Data from Resistance Spot Weld Process Monitoring For Adaptive Welding 自适应焊接中电阻点焊过程监测超声数据的实时人工智能驱动解释

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-07-01 DOI: 10.32548/2023.me-04344

R. Scott, D. Stocco, A. Chertov, Roman Gr. Maev

{"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}

引用次数: 0

Benefits and Concerns of Using Emerging Artificial Intelligence Chatbots With Work in NDT 在无损检测中使用新兴人工智能聊天机器人的好处和关注

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-07-01 DOI: 10.32548/2023.me-04361

John Aldrin

引用次数: 0

Tips for Effective Machine Learning in NDT/E 无损检测/无损检测中有效的机器学习技巧

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-07-01 DOI: 10.32548/2023.me-04358

J. Harley, S. Zafar, Charlie Tran

引用次数: 0

Acoustic Emission Source Localization using Deep Transfer Learning and Finite Element Modeling–based Knowledge Transfer 基于深度迁移学习和有限元建模知识迁移的声发射源定位

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-07-01 DOI: 10.32548/2023.me-04348

Xuhui Huang, Obaid Elshafiey, Karim Farzia, L. Udpa, Ming Han, Y. Deng

{"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}

引用次数: 0

Validated and Deployable AI/ML for NDT Data Diagnostics 用于无损检测数据诊断的验证和可部署的AI/ML

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-07-01 DOI: 10.32548/2023.me-04364

E. Lindgren

引用次数: 1

Machine Learning Techniques for Acoustic Data Processing in Additive Manufacturing In Situ Process Monitoring: A Review 增材制造现场过程监测中声学数据处理的机器学习技术综述

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-07-01 DOI: 10.32548/2023.me-04356

H. Taheri, S. Zafar

{"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}

引用次数: 1

A Novel Method for Distinguishing Discontinuities In Ferromagnetic Materials Based On Eddy Current Testing Under Magnetization 基于磁化条件下涡流检测的铁磁材料不连续性判别新方法

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-06-01 DOI: 10.32548/2023.me-04284

G. Jia, Pengchao Chen, Rui Li, Kuan-Chung Fu, Rongbiao Wang, K. Song

{"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}

引用次数: 0

Phased Array Ultrasonic Testing of Stainless Steel Pipe Welds 不锈钢管焊缝的相控阵超声检测

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-06-01 DOI: 10.32548/2023.me-04333

A. Birring, James Williams

引用次数: 0

Evaluation of Influence of Surface Roughness Orientation in CFRP Lap Joints using AE and DIC 基于声发射和DIC的CFRP搭接表面粗糙度取向影响评价

IF 0.6 4区材料科学

Materials Evaluation Pub Date : 2023-06-01 DOI: 10.32548/2023.me-04326

L. S. Mane, M. Bhat

引用次数: 0