ASNT 30th Research Symposium Conference Proceedings最新文献_第2页

Subwavelength Microwave Imaging System using a Negative Index Metamaterial Lens 使用负折射率超材料透镜的亚波长微波成像系统

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.007

Srijan Datta, Xiaodong Shi, Yiming Deng, L. Udpa

引用次数: 2

Extracting Non-Propagating Modes in Waveguides via Electro-Mechanical Impedance Techn 利用机电阻抗技术提取波导中的非传播模式

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.017

Keping Zhang, Ranting Cui, Xuan Zhu

引用次数: 0

Modeling Process-Structure Relationships for Additively Manufactured Microscale Features 增材制造微尺度特征的工艺结构关系建模

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.013

E. Jost, J. Pegues, D. Moore, C. Saldana

{"title":"Modeling Process-Structure Relationships for Additively Manufactured Microscale Features","authors":"E. Jost, J. Pegues, D. Moore, C. Saldana","doi":"10.32548/rs.2022.013","DOIUrl":"https://doi.org/10.32548/rs.2022.013","url":null,"abstract":"Laser powder bed fusion (LPBF) additive manufacturing (AM) presents a unique opportunity to create geometries, such as lattice structures, which are impossible to manufacture using traditional methods. Lattice structures are favored for their high strength-to-weight ratios, tunable and gradable properties, and energy absorption capacity. However, due to their feature size, (e.g., struts/walls as small as 200 µm), lattice performance is detrimentally impacted by the surface topography, defects, and heterogeneities characteristic of LPBF, which are inextricably linked to manufacturing parameters. While the performance impacts of these defects is understood to be severe, the mechanisms of their creation, manufacturing strategies for mitigation, and effects on performance are either underdeveloped or not yet fully understood. To address this knowledge gap, this study focuses on understanding the influence of manufacturing parameters on structural outcomes by modeling the process-structure (PS) relationships in microscale LPBF features. Herein, it is demonstrated that statical and machine learning models can predict geometric characteristics of lattices with up to 98% accuracy.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115307962","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}

引用次数: 0

Irregular Frequencies in Ultrasonic Boundary Integral Equations for Spherical Scatterer 球面散射体超声边界积分方程中的不规则频率

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.026

P. Gurrala, Jiming Song

引用次数: 0

Using Deep Learning to Characterize Ultrasonic B-scans 利用深度学习表征超声b扫描

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.002

R. Scott, D. Stocco, A. Chertov

{"title":"Using Deep Learning to Characterize Ultrasonic B-scans","authors":"R. Scott, D. Stocco, A. Chertov","doi":"10.32548/rs.2022.002","DOIUrl":"https://doi.org/10.32548/rs.2022.002","url":null,"abstract":"Though manufacturing is becoming increasingly technologically advanced, statistical destructive and nondestructive evaluation (NDE) are still the dominant methods for quality control and Industry/NDE 4.0 is still not fully realized. Ultrasonic inspection systems are increasingly used, but there is still a need for fast, automated, and accurate data interpretation. To this end, the IDIR has developed an approach for ultrasonic B-scan interpretation using deep learning (DL) which is a form of artificial intelligence (AI) using deep artificial neural networks to automatically learn from data. DL forms the state of the art in many problem domains in e.g. natural language processing and computer vision, hence it has become increasingly, and often successfully, applied in NDE. Our aim was to investigate a DL approach to automatic characterization of ultrasonic B-scans. We experimented on ultrasonic B-scans from resistance spot welding (RSW) because we could rapidly generate a large dataset of samples using this process. We developed and labelled a dataset of ultrasonic B-scans from RSWs of varying parameterizations, along with important metadata (e.g. sheet thicknesses, weld time, etc.), and subsequently trained DL models for object detection on the labelled samples. The resultant AI system conducts a morphological analysis of the weld geometry after the weld is completed. Using an object detection approach, we created models that exhibit high detection rates with extremely low false positive rates, while accurately measuring the position of the nugget within the welded stack. Our work shows the applicability of DL in real-time NDE data interpretation. Such AI-based systems can be combined with ultrasonic NDE to comprehensively, accurately, and practically instantly characterize 100% of parts without human intervention, representing a major step toward Industry/NDE 4.0 and zero-defect RSW.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131529338","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}

引用次数: 0

21st Century NDE Revealing Moments … The Stakes Are High 21世纪的濒死体验时刻…风险很高

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.035

Surendra Singh, Eric L. Lanigan, D. Beckett

引用次数: 0

Intelligence Augmentation for Aviation-based NDE Data 基于航空的NDE数据的智能增强

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.005

E. Lindgren, J. Aldrin, D. Forsyth

{"title":"Intelligence Augmentation for Aviation-based NDE Data","authors":"E. Lindgren, J. Aldrin, D. Forsyth","doi":"10.32548/rs.2022.005","DOIUrl":"https://doi.org/10.32548/rs.2022.005","url":null,"abstract":"With the increased availability of digital data from nondestructive evaluation (NDE) systems, there is a natural inquisitiveness to explore the use of statistical regression and classification methods for NDE data. A continuous issue for Artificial Intelligence/Machine Learning (AI/ML) methods is a question of how much data is required to enable training and how high of fidelity is required for such training. The challenge of relevant NDE-based data for aviation applications is not trivial. There are limited data sets as typical areas with flaws, such as fatigue cracks or corrosion, are repaired as soon as they are detected. Another challenge with USAF specific aviation NDE data is the broad range of variables that affect the data. To address the limitations of available data, the approach taken by the US Air Force (USAF) NDE community is to integrate attributes of AI/ML with other algorithms for analysis of NDE data, plus integrating human analysis into the final decision making process. The combination of both statistical analysis of data combined with human analysis to determine if flaws are present has been named Intelligence Augmentation (IA). The USAF has a rich history of using IA to analyze large NDE data sets, typically acquired from inspections that use automated scanning to acquire data. USAF research continues in the area of IA for various applications. Future opportunities will include improved integration of models, especially as a function of their maturity through validation.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132167407","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}

引用次数: 1

Nondestructive Detection of In-Plane Wrinkles in Carbon Fiber Laminates Using Eddy Current and Ultrasonic Testing 涡流与超声无损检测碳纤维层合板面内褶皱

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.038

Matthew Newton, Tonoy Chowdhury, I. Gravagne, D. Jack

{"title":"Nondestructive Detection of In-Plane Wrinkles in Carbon Fiber Laminates Using Eddy Current and Ultrasonic Testing","authors":"Matthew Newton, Tonoy Chowdhury, I. Gravagne, D. Jack","doi":"10.32548/rs.2022.038","DOIUrl":"https://doi.org/10.32548/rs.2022.038","url":null,"abstract":"Nondestructive detection of the in-plane wrinkles (sometimes termed marcelling in various industrial settings) of unidirectional carbon fiber reinforced plastic (CFRP) laminates is of interest in a wide variety of industries as the in-plane wrinkles significantly reduce the mechanical performance of the manufactured composite structure. In this work, a comparative study is presented to detect in-plane wrinkles on a 5 ply unidirectional CFRP laminate with a customized eddy current testing (ECT) and ultrasonic testing (UT) system. A manufacturing method to inducing controlled marcelling within a specified lamina is introduced, and fabricated components are then inspected using both ECT and UT. Data suggests that the results from the directionally biased ECT system effectively captures both the presence of an in-plane wrinkle and quantify the shape of the wrinkle, whereas the results from the ultrasonic inspection were not able to properly quantify the wrinkle extent. Using the anisotropic conductive property of CFRP, the eddy current system was able to clearly detect spatial variations in the local fiber orientation, waviness steepness angle, and waviness amplitude, three different parameters used to quantify the wrinkle impact on the structural performance.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129321631","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}

引用次数: 0

Uncertainty quantification in immersion ultrasound measurements using a Bayesian inferencing approach 使用贝叶斯推理方法的浸入式超声测量中的不确定度量化

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.025

S. Mukherjee, Hillary R. Fairbanks, J. Lum, David S. Stobbe, Seeman Karimi, J. Tringe

{"title":"Uncertainty quantification in immersion ultrasound measurements using a Bayesian inferencing approach","authors":"S. Mukherjee, Hillary R. Fairbanks, J. Lum, David S. Stobbe, Seeman Karimi, J. Tringe","doi":"10.32548/rs.2022.025","DOIUrl":"https://doi.org/10.32548/rs.2022.025","url":null,"abstract":"Ultrasound nondestructive evaluation (NDE) methods often use a deterministic inverse model to reconstruct material properties. Such techniques rely on accurate information about the material such as wave-speed and attenuation at different frequencies, as well as information about the measurement system such as transducer radiation properties and measurement noise. However, in reality there is uncertainty associated with each of these important quantities. This is particularly important for structures manufactured using advanced manufacturing techniques since the mechanical properties of materials in these structures can vary significantly across the manufactured object. Prior work in uncertainty quantification for ultrasound NDE has been mostly limited to either simulation datasets for guided wave or resonant ultrasound measurements in metals prepared using conventional subtractive manufacturing techniques. Here we describe a new process for quantifying and incorporating the uncertainty in metal additive manufacturing from immersion ultrasound measurements and demonstrate that this can better defect detection with higher accuracy and confidence.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130357503","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}

引用次数: 0

Total Focusing Method with Laser-Generated Ultrasonic Waves for Defect Detection in Finite Plates 激光超声全聚焦法检测有限板缺陷

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.024

Megan E. McGovern, Dmitriy D. Bruder, R. James, V. Gattani

引用次数: 3