ASNT 30th Research Symposium Conference Proceedings最新文献_第3页

A hybrid physics-based/data-driven damage detection method for Lamb wave structural health monitoring 基于物理/数据驱动的Lamb波结构健康监测混合损伤检测方法

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.030

Hamed Momeni, Arvin Arvin Ebrahimkhanlou

引用次数: 0

Applying Acoustic Emission to Large Format Additive Manufacturing 声发射技术在大幅面增材制造中的应用

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.015

Ryan Spencer, Miguel González Núñez, H. Saboonchi, V. Godínez-Azcuaga, U. Vaidya, V. Kunc, A. Hassen

{"title":"Applying Acoustic Emission to Large Format Additive Manufacturing","authors":"Ryan Spencer, Miguel González Núñez, H. Saboonchi, V. Godínez-Azcuaga, U. Vaidya, V. Kunc, A. Hassen","doi":"10.32548/rs.2022.015","DOIUrl":"https://doi.org/10.32548/rs.2022.015","url":null,"abstract":"Large format additive manufacturing (AM) is being adapted as a method of producing large structures in a short lead time and cost-effective way. With the growing advancement in AM techniques and application, machine monitoring and part qualification is highly needed. There has been leading research focused on the manufacturing development and feedstock material but minimum research on the structural health monitoring (SHM), defect detection, and nondestructive evaluation (NDE) for large format AM. Scanning large structure using conventional nondestructive testing (NDT) techniques, such as ultrasound or X-ray, and searching for potential defects can be very time consuming, challenging and cost prohibitive. Acoustic emission (AE) is a passive technique that can be used to monitor and locate defect progression in large structures by distributing group of sensors around the part. This research outlines the necessary procedure for implementing AE to AM as a reliability method. The topics this research will display are: (a) Wave propagation/velocity evaluation, (b) an AE attenuation characterization for the anisotropic printed structure and material, (c) background noise measurements of the extruder and gantry system, and (d) assist in optimal sensor selection and placement for monitoring large AM structures with demonstration. This work establishes the foundation for scaling up the SHM-AE system for the large additive platform.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"231 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":"124554478","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

Labeling Defective Regions in In-situ Optical Tomography Images 原位光学层析成像中缺陷区域的标记

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.004

Mead Dennison, Connor Seavers, T. Chu

{"title":"Labeling Defective Regions in In-situ Optical Tomography Images","authors":"Mead Dennison, Connor Seavers, T. Chu","doi":"10.32548/rs.2022.004","DOIUrl":"https://doi.org/10.32548/rs.2022.004","url":null,"abstract":"Methods for automatically detecting defects are highly sought after in the world of non-destructive testing and evaluation (NDT&E). Machine Learning (ML) and Deep Learning (DL) algorithms have performed well in this area but often require labeled training examples [1, 2]. This investigation aims to provide insight into the process of obtaining labeled training examples from NDE image data for application to ML and DL. Training data typically consists of the raw NDE data and its corresponding labels. When the NDE data is in the form of an image, the labels can be binary masks, bounding boxes, or semantically segmented images, to name a few. What precisely the labels are labeling depends on the goals in mind. When the goal is the detection of defects, label production might entail binary masking of defective regions, defining bounding boxes around defective regions, or semantically segmenting the image into background, foreground, and defect regions. The performance of a given ML/DL model depends on the quality of the features used for training [3]. The need for accurate defect detection methodologies is particularly stark in metal additive manufacturing (AM) processes, which are prone to producing numerous and disparate process defects. This propensity for metal AM processes to produce defects severely limits their incorporation into end-use component production lines. Methodologies that can accurately detect defects during the manufacturing process are critical to additively manufactured component qualification efforts. This investigation details one step in the ML/DL training process, specifically the defect labeling process, applied to optical tomography images obtained from in-situ monitoring the selective laser melting (SLM) process. The entire image processing workflow entails binary segmentation (masking) of the defective regions, estimation of the defect contours, and estimation of the bounding boxes for defects in the image.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"32 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":"127088721","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

Automated Interpretation of Impact Echo Data using Statistical Modeling and Machine Learning 利用统计建模和机器学习自动解释冲击回波数据

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.028

Agnimitra Sengupta, Rahul Torlapati, Hoda Azari, Ilgin Guler, P. Shokouhi

{"title":"Automated Interpretation of Impact Echo Data using Statistical Modeling and Machine Learning","authors":"Agnimitra Sengupta, Rahul Torlapati, Hoda Azari, Ilgin Guler, P. Shokouhi","doi":"10.32548/rs.2022.028","DOIUrl":"https://doi.org/10.32548/rs.2022.028","url":null,"abstract":"Impact echo (IE) is becoming a standard nondestructive testing (NDT) tool for concrete bridge deck assessment thanks to its simplicity, established reliability and relatively low cost. The recent advances in automated data collection and non-contact measurements [1] [2] are also contributing to IE’s increasing popularity. The new generation of IE test equipment uses robotic platforms allowing faster data collection and larger areal coverage compared to the hand-held devices previously employed. The large volume of collected data presents numerous opportunities but also new challenges. The availability of NDT data facilitates data-driven decision making from IE (and other NDT) data while new approaches are needed to process and interpret ‘big’ NDT data [3] [4]. The former has been addressed for example in our recently published work, where IE data collected during Long Term Bridge Performance (LTBP) program are used to predict condition rating (CR) of concrete bridge decks [5]. This study focuses on the latter, the increasingly urgent need to automatically analyze and interpret IE data using statistical modeling, machine learning (ML) and deep learning (DL). We present results pertaining to the analyses of LTBP data without ground truth as well as those obtained on laboratory slabs with well-defined embedded defects [6]. The performance of different methods in IE signal classification is compared and discussed. Our findings indicate that the performance of different methods greatly depends on the amount and quality of available ‘labeled’ data (i.e., data tagged with the corresponding ground truth information). Creating standard quality labeled datasets is a critical step in exploiting ML and DL for IE (and other NDT) data analysis and interpretation.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"12 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":"131783719","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

Railroad Crosstie Deflection Measurement via Ultrasonic Airborne Sonar and Computer Vision Techniques 基于超声机载声纳和计算机视觉技术的铁路横向偏转测量

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.011

A. Hosseinzadeh, D. Datta, F. L. di Scalea

{"title":"Railroad Crosstie Deflection Measurement via Ultrasonic Airborne Sonar and Computer Vision Techniques","authors":"A. Hosseinzadeh, D. Datta, F. L. di Scalea","doi":"10.32548/rs.2022.011","DOIUrl":"https://doi.org/10.32548/rs.2022.011","url":null,"abstract":"A smart tie-tracking technology is proposed to measure the deflections of railroad crossties by means of non-contact ultrasonic testing in sonar mode and computer vision techniques. The sensing layout consists of an array of air-coupled capacitive transducers (in pulse-echo mode) and a high frame-rate camera, rigidly connected to the main frame of train car. The acquisition system is programmed such that the synchronized waveforms and images are collected and saved as train car moves. In the processing stage, a machine learning-based image classification approach is developed to discriminate tie/ballast images and demarcate the crossties’ boundaries. The relative deflections of the identified crossties are eventually computed by tracking the arrival time of the reflected waves from the surfaces flagged as tie. Further inspection of the deflection profiles can reveal crossties with potential poor ballast support condition. The proposed ‘tie sonar’ system was prototyped and used to reconstruct the deflection profile of the crossties scanned during a series of test runs at the Rail Defect Testing Facility of UC San Diego as well as the BNSF yard in San Diego, CA.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"15 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":"134130131","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

A Quantitative Assessment of Historical Nondestructive Evaluation (NDE) Probability of Detection (POD) Data for Railroad Tank Cars 铁路罐车历史无损评估(NDE)检测概率(POD)数据的定量评估

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.018

Anish Poudel, Silvia Galván-Núñez, Brian Lindeman, Fancisco Gonzalez

引用次数: 0

Detection and Characterization of Rolling Contact Fatigue Type of Defects Using Rayleigh Surface Waves 基于瑞利表面波的滚动接触疲劳型缺陷检测与表征

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.019

Alex T Vu, Yoganandh Madhuranthakam, S. Chakrapani

引用次数: 0

Interlayer lack of fusion Characterization of Direct Energy Deposition Additive Manufactured (AM) Components using Nonlinear Ultrasound 用非线性超声表征直接能量沉积增材制造(AM)元件的层间缺乏融合

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.031

Rose Ghasemi, E. Dehghan-Niri

{"title":"Interlayer lack of fusion Characterization of Direct Energy Deposition Additive Manufactured (AM) Components using Nonlinear Ultrasound","authors":"Rose Ghasemi, E. Dehghan-Niri","doi":"10.32548/rs.2022.031","DOIUrl":"https://doi.org/10.32548/rs.2022.031","url":null,"abstract":"Metal-based additive manufacturing (AM) has recently gotten a lot of interest in the automotive, oil and gas, aviation, and aerospace industries for constructing complicated components and parts. One of the most critical types of defect in products manufactured using this method is interlayer lack of fusion of the printed components. The traditional ultrasound techniques are unable to reliably detect and quantify such defects if the impedance mismatch of the defects with the surrounding materials is not significant. The interlayer lack of fusion transmits the majority of the sound energy in traditional ultrasonic testing procedures, reducing detection capabilities in pulse-echo or transmission inspection modes. However, nonlinear ultrasound techniques have shown promise in detecting similar defects like fatigue cracks that have the same limiting detection condition. Usually changes in the frequency domain are considered as damage sensitive feature for defect detection and evaluation. In this study, phase-space domain is used as a complementary domain to frequency domain to investigate the behavior of nonlinear ultrasound waves because of internal lack of fusion in the Direct Energy Deposition (DED) additive manufacturing process. the early experimental results on samples showed that this technique can be utilized to characterize the interlayer lack of fusion in the parts created using the metal-based AM process.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"46 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":"131751733","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

Machine learning-based damage detection of RC wall using graph features of crack patterns 基于机器学习的钢筋混凝土墙体裂纹图特征损伤检测

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.003

Pedram Bazrafshan, Thinh On, A. Ebrahimkhanlou

{"title":"Machine learning-based damage detection of RC wall using graph features of crack patterns","authors":"Pedram Bazrafshan, Thinh On, A. Ebrahimkhanlou","doi":"10.32548/rs.2022.003","DOIUrl":"https://doi.org/10.32548/rs.2022.003","url":null,"abstract":"Concrete crack quantification is one of the challenges that has been investigated. In this article, a computer vision method is used to detect and quantify the cracks on a concrete surface. After processing the crack images, cracks are modeled as graphs for feature extraction. To study the proposed method, concrete surface crack images from a reinforced concrete shell under quasi-linear load at each load step are used. Having the graph and mechanical features, a PCA analysis is performed to study the dependency of the features. Using GPC as graph principal components and MPC as mechanical principal components, a linear Pearson correlation analysis is performed on the GPC and MPC data, results of which demonstrates more than 75% consistency. Finding the graph features in inherent relationship with the mechanical features, the paper continues with a machine learning study between the two features. Due to the low in-hand data, two different machine learning algorithms are used for the verification purpose. Results of the linear regression model and leave-one-out model showed a very close accuracy with 1% and 2% error, respectively. All findings attest the novel idea of presenting graph features. Graph features can be interpreted to use as a representative for mechanical features. Moreover, this method provides the opportunity of studying crack from a mathematical and fundamental viewpoint.","PeriodicalId":367504,"journal":{"name":"ASNT 30th Research Symposium Conference Proceedings","volume":"35 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":"132195640","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

A Remote Temperature Measurement Using FSS-Based Sensing 基于fss传感的远程温度测量

ASNT 30th Research Symposium Conference Proceedings Pub Date : 1900-01-01 DOI: 10.32548/rs.2022.008

S. Ramesh, K. Donnell

引用次数: 0