Industrial gas turbines are robust, durable, and dependable, but they can develop problems such as internal wear, loss of thermal barrier coatings, and premature part failures. If left undetected, these issues can lead to significant unplanned costs and downtime. To prevent premature failures, and as an aid in future outage planning, internal health monitoring using remote visual inspection (RVI) can determine whether components are in good condition and fit for service, or if additional repairs are needed. The use of RVI, with a video borescope capable of analyzing and quantifying indications usng 3D data displayed in a point cloud, allows for measuring anomalies with accuracies of 0.001 in. (0.025 mm). In some cases, early detection and 3D analysis of internal issues in industrial gas turbines have saved operators millions of dollars, sometimes even during a single outage.
{"title":"RVI For Internal Health Monitoring Of Industrial Gas Turbines","authors":"Paul Thompson","doi":"10.32548/2024.me-04450","DOIUrl":"https://doi.org/10.32548/2024.me-04450","url":null,"abstract":"Industrial gas turbines are robust, durable, and dependable, but they can develop problems such as internal wear, loss of thermal barrier coatings, and premature part failures. If left undetected, these issues can lead to significant unplanned costs and downtime. To prevent premature failures, and as an aid in future outage planning, internal health monitoring using remote visual inspection (RVI) can determine whether components are in good condition and fit for service, or if additional repairs are needed. The use of RVI, with a video borescope capable of analyzing and quantifying indications usng 3D data displayed in a point cloud, allows for measuring anomalies with accuracies of 0.001 in. (0.025 mm). In some cases, early detection and 3D analysis of internal issues in industrial gas turbines have saved operators millions of dollars, sometimes even during a single outage.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846115","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}
Machine vision systems and other visual inspection methods are commonly used in the automotive industry for manufacturing, service, and repair. This article is focused on nondestructive visual testing methods for vehicle service and repair.
{"title":"Machine Vision–Based Tools for Automotive Service and Repair","authors":"Daniel Lau","doi":"10.32548/2024.me-04447","DOIUrl":"https://doi.org/10.32548/2024.me-04447","url":null,"abstract":"Machine vision systems and other visual inspection methods are commonly used in the automotive industry for manufacturing, service, and repair. This article is focused on nondestructive visual testing methods for vehicle service and repair.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838758","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}
E. Zwicker, Brandon Deboer, Markus Weissmann, Antoine Chevaleyre
Robotic visual inspection presents a promising solution to the challenges posed by confined space inspection, offering enhanced efficiency, accuracy, and safety.
{"title":"Robotic Visual Inspection in Confined Spaces","authors":"E. Zwicker, Brandon Deboer, Markus Weissmann, Antoine Chevaleyre","doi":"10.32548/2024.me-04454","DOIUrl":"https://doi.org/10.32548/2024.me-04454","url":null,"abstract":"Robotic visual inspection presents a promising solution to the challenges posed by confined space inspection, offering enhanced efficiency, accuracy, and safety.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853780","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}
Most major nondestructive testing (NDT) personnel qualification and certification (PQ&C) schema address visual testing (VT) as a standalone NDT method. However, there are significant differences between the details of these elements. Various codes, standards, and specifications delineate various requirements for personnel education, experience, training, and examination of the candidates for certification. This article addresses the common elements needed for PQ&C across different codes, standards, and guidelines.
{"title":"Visual Testing Method Personnel Qualification and Certification: An Overview","authors":"Mike Allgaier","doi":"10.32548/2024.me-04446","DOIUrl":"https://doi.org/10.32548/2024.me-04446","url":null,"abstract":"Most major nondestructive testing (NDT) personnel qualification and certification (PQ&C) schema address visual testing (VT) as a standalone NDT method. However, there are significant differences between the details of these elements. Various codes, standards, and specifications delineate various requirements for personnel education, experience, training, and examination of the candidates for certification. This article addresses the common elements needed for PQ&C across different codes, standards, and guidelines.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842557","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}
Modern robotic crawlers provide a means of safe and effective remote data capture, reducing confined space entries, minimizing data collection times, enhancing inspection data quality, and optimizing overall inspection spend.
{"title":"Robotic Crawlers For Visual Testing","authors":"Ron Kessler","doi":"10.32548/2024.me-04448","DOIUrl":"https://doi.org/10.32548/2024.me-04448","url":null,"abstract":"Modern robotic crawlers provide a means of safe and effective remote data capture, reducing confined space entries, minimizing data collection times, enhancing inspection data quality, and optimizing overall inspection spend.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843873","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}
A range of ultrasonic techniques associated with the nondestructive evaluation of metals involves the propagation of low-frequency elastic waves. Metals that are isotropic and homogeneous in the macroscopic length scale contain elastic heterogeneities, such as grain boundaries within the microstructures. Ultrasonic waves propagating through such microstructures get scattered from the grain boundaries. As a result, the propagating ultrasound attenuates. The mass density and the elastic anisotropy in each constituent grain govern the degree of heterogeneity in the polycrystalline aggregates. Existing elastodynamic models consider first-order scattering effects from grain boundaries. This paper presents the improved attenuation formulae, for the first time, by including the next order of grain scattering effects. Results from investigating 759 polycrystals reveal a positive correlation between the effects of higher-order scattering from grain boundaries and the degree of heterogeneity. Thus, higher-order grain scattering effects are now known. These results motivate further investigation into higher frequencies and strongly scattering alloys in the future.
{"title":"Improved Formulae for Low-Frequency Ultrasonic Attenuation in Metals","authors":"Anubhav Roy, Christopher M. Kube","doi":"10.32548/2024.me-04422","DOIUrl":"https://doi.org/10.32548/2024.me-04422","url":null,"abstract":"A range of ultrasonic techniques associated with the nondestructive evaluation of metals involves the propagation of low-frequency elastic waves. Metals that are isotropic and homogeneous in the macroscopic length scale contain elastic heterogeneities, such as grain boundaries within the microstructures. Ultrasonic waves propagating through such microstructures get scattered from the grain boundaries. As a result, the propagating ultrasound attenuates. The mass density and the elastic anisotropy in each constituent grain govern the degree of heterogeneity in the polycrystalline aggregates. Existing elastodynamic models consider first-order scattering effects from grain boundaries. This paper presents the improved attenuation formulae, for the first time, by including the next order of grain scattering effects. Results from investigating 759 polycrystals reveal a positive correlation between the effects of higher-order scattering from grain boundaries and the degree of heterogeneity. Thus, higher-order grain scattering effects are now known. These results motivate further investigation into higher frequencies and strongly scattering alloys in the future.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141389771","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 lead seal is one of the piezoelectric cable components that is prone to failure. In this paper, the accumulated value of pulsed eddy current signals was utilized to characterize the subsurface defect depth of the lead seal for the safety of the lead seal in service. The characterization method of the defect depth was analyzed by the finite element model. The simulation results showed that the accumulated value feature had a linear relationship with the subsurface defect depth in a semilogarithmic coordinate system, and the linear fitting coefficient was 0.9982. The experimental results showed that the accuracy of the accumulated value feature in characterizing the subsurface defect depth was better than that of the peak value feature. Specifically, the maximum relative errors of the measurements were 12.51% and 9.07%, while the average relative errors were 5.83% and 8.00%, respectively.
{"title":"Depth Evaluation of Subsurface Defects in Lead Seal Based on Accumulated Value Feature from Pulsed Eddy Current Signals","authors":"Qianqiu Shao, Songhai Fan, Fenglian Liu","doi":"10.32548/2024.me-04401","DOIUrl":"https://doi.org/10.32548/2024.me-04401","url":null,"abstract":"The lead seal is one of the piezoelectric cable components that is prone to failure. In this paper, the accumulated value of pulsed eddy current signals was utilized to characterize the subsurface defect depth of the lead seal for the safety of the lead seal in service. The characterization method of the defect depth was analyzed by the finite element model. The simulation results showed that the accumulated value feature had a linear relationship with the subsurface defect depth in a semilogarithmic coordinate system, and the linear fitting coefficient was 0.9982. The experimental results showed that the accuracy of the accumulated value feature in characterizing the subsurface defect depth was better than that of the peak value feature. Specifically, the maximum relative errors of the measurements were 12.51% and 9.07%, while the average relative errors were 5.83% and 8.00%, respectively.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409015","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 the 21st century of NDE technology and deployment, monitoring technologies and strategies are completely different than inspection technologies and strategies. Monitoring is no longer sending a technician to a location to take repeated manual data – this is still inspection, just more frequently. Monitoring takes place after a manual/automated inspection has located an anomaly and instrumentation (a sensor in this case), is installed to take recurring readings at that precise location to gather a large sample size of data to be analyzed. These two strategies, when employed in concert, even only on the most critical (10%) circuits/tanks/units, are optimizing asset health, reducing unplanned downtime, and eliminating the need to have people in potentially unsafe situations to the tune of tens of millions of dollars per year. This article will provide real life examples of the ~20% of the best run refineries, chemical, paper, and power plants which have cracked the code and are successfully using an inspect to monitor strategy to change the game of how they implement asset integrity in the 21st century. As you read this article, ask yourself the question, is my facility a part of this ~20%? If not, what can I start doing, and what incremental step changes can I make to my legacy inspection & asset integrity strategy, to use technology to spend my precious budget and resources to be a part of this ~20% in 2024 and beyond? Many asset owners struggle to identify the root cause of fluctuating corrosion rates due to unreliable, infrequent, or sheer lacks of quantity (and quality) of inspection data to make informed decisions on asset health. Facilities worldwide are tasked with monitoring thousands of Condition Monitoring Locations (CMLs) with established NDE techniques such as manual ultrasonic testing and radiography. While these techniques can provide valuable “snapshots” of the condition of particular locations, limitations and inherent errors can compound leading to ill-advised decision making. Manually taken thickness data can vary greatly and result in unwarranted complacency or excessive and costly inspections. Utilizing long range wireless continuous monitoring systems have drastically improved operators understanding of how process changes influence corrosion rates. Installed UT sensors provide near real-time data with a 0.001” accuracy. This influx of data provides a trend line whereas with manually taken data, sometimes only taken a few times a year, only provides a few points scattered across a graph. Asset owners have realized they can use this wealth of new information to validate and discover the effects of operational changes on corrosion rates and make more informed decisions to impact asset extension, retirement, and increasing the efficiency of assets during its useful life.
{"title":"Working Smart versus Working Hard: Wireless UT Sensors for Asset Integrity Monitoring","authors":"Steve Strachan","doi":"10.32548/2024.me-04409","DOIUrl":"https://doi.org/10.32548/2024.me-04409","url":null,"abstract":"In the 21st century of NDE technology and deployment, monitoring technologies and strategies are completely different than inspection technologies and strategies. Monitoring is no longer sending a technician to a location to take repeated manual data – this is still inspection, just more frequently. Monitoring takes place after a manual/automated inspection has located an anomaly and instrumentation (a sensor in this case), is installed to take recurring readings at that precise location to gather a large sample size of data to be analyzed. These two strategies, when employed in concert, even only on the most critical (10%) circuits/tanks/units, are optimizing asset health, reducing unplanned downtime, and eliminating the need to have people in potentially unsafe situations to the tune of tens of millions of dollars per year. This article will provide real life examples of the ~20% of the best run refineries, chemical, paper, and power plants which have cracked the code and are successfully using an inspect to monitor strategy to change the game of how they implement asset integrity in the 21st century. As you read this article, ask yourself the question, is my facility a part of this ~20%? If not, what can I start doing, and what incremental step changes can I make to my legacy inspection & asset integrity strategy, to use technology to spend my precious budget and resources to be a part of this ~20% in 2024 and beyond?\u0000\u0000Many asset owners struggle to identify the root cause of fluctuating corrosion rates due to unreliable, infrequent, or sheer lacks of quantity (and quality) of inspection data to make informed decisions on asset health. Facilities worldwide are tasked with monitoring thousands of Condition Monitoring Locations (CMLs) with established NDE techniques such as manual ultrasonic testing and radiography. While these techniques can provide valuable “snapshots” of the condition of particular locations, limitations and inherent errors can compound leading to ill-advised decision making. Manually taken thickness data can vary greatly and result in unwarranted complacency or excessive and costly inspections. Utilizing long range wireless continuous monitoring systems have drastically improved operators understanding of how process changes influence corrosion rates. Installed UT sensors provide near real-time data with a 0.001” accuracy. This influx of data provides a trend line whereas with manually taken data, sometimes only taken a few times a year, only provides a few points scattered across a graph. Asset owners have realized they can use this wealth of new information to validate and discover the effects of operational changes on corrosion rates and make more informed decisions to impact asset extension, retirement, and increasing the efficiency of assets during its useful life.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141408424","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}
Erqing Zhang, S. Wang, Shengrong Zhou, Yannan Li, Shunzhou Huang, Tao Ma
The evaluation of the size of weld defects is crucial in assessing the quality of weld structures. In this study, a novel quantitative evaluation method for weld defects was proposed based on 3D reconstruction using slices. The method includes two significant contributions. First, the supervised defect segmentation method was introduced, which uses the results of the previous slice to supervise the segmentation of the current slice based on slight changes in adjacent defect slices. This ensures accurate segmentation of all defects. Second, the subpixel edge extraction method combining the Canny operator and cubic spline interpolation was proposed to improve the accuracy of edge detection. The proposed method was evaluated using 15 defects. The experimental results showed that the average errors of inclusion defects, incomplete-penetration defects, and incomplete-fusion defects were 13.6%, 8.18%, and 13.9%, respectively. Compared with the other methods, the proposed method not only had higher accuracy but also provided the volume value of defects.
{"title":"Quantitative Evaluation Of Weld Defects Based On Overall Shape Three-Dimensional Reconstruction","authors":"Erqing Zhang, S. Wang, Shengrong Zhou, Yannan Li, Shunzhou Huang, Tao Ma","doi":"10.32548/2024.me-04357","DOIUrl":"https://doi.org/10.32548/2024.me-04357","url":null,"abstract":"The evaluation of the size of weld defects is crucial in assessing the quality of weld structures. In this study, a novel quantitative evaluation method for weld defects was proposed based on 3D reconstruction using slices. The method includes two significant contributions. First, the supervised defect segmentation method was introduced, which uses the results of the previous slice to supervise the segmentation of the current slice based on slight changes in adjacent defect slices. This ensures accurate segmentation of all defects. Second, the subpixel edge extraction method combining the Canny operator and cubic spline interpolation was proposed to improve the accuracy of edge detection. The proposed method was evaluated using 15 defects. The experimental results showed that the average errors of inclusion defects, incomplete-penetration defects, and incomplete-fusion defects were 13.6%, 8.18%, and 13.9%, respectively. Compared with the other methods, the proposed method not only had higher accuracy but also provided the volume value of defects.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140275529","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}
Shaofeng Wang, Yaowen Zhang, Shenrong Zhou, Wenjing Liu, Fei Du, Jian Wang, Fei Hui, Mingyuan Yang
An image reconstruction method based on the multimode total focusing method (MTFM) is proposed to overcome the limitations of traditional total focusing method (TFM) imaging in detecting tiny discontinuities at complex locations. We conducted MTFM detection and TFM image reconstruction experiments for irregular welds containing multiple discontinuities. In an experiment using four 1 mm diameter manufactured defects fabricated on two aluminum alloy welded test blocks, we achieved two significant contributions. First, we accurately detected small discontinuities by combining CIVA simulation with robotic arm assistance. Second, we proposed fusion factor coefficients for TFM image processing, which considered different modal weights for image fusion and de-noising, thereby preserving the integrity of the fused images. Our experimental results demonstrate that the reconstructed TFM images effectively represented all defect information. Compared with other modal TFM images with the highest signal-to-noise ratio (SNR), the amplitude-corrected optimized TFM image exhibits an improved SNR of 51.95% without losing defect information.
{"title":"Visualizing Defects of Irregular Weld Seams Based on MultiMode TFM Detection","authors":"Shaofeng Wang, Yaowen Zhang, Shenrong Zhou, Wenjing Liu, Fei Du, Jian Wang, Fei Hui, Mingyuan Yang","doi":"10.32548/2024.me-04391","DOIUrl":"https://doi.org/10.32548/2024.me-04391","url":null,"abstract":"An image reconstruction method based on the multimode total focusing method (MTFM) is proposed to overcome the limitations of traditional total focusing method (TFM) imaging in detecting tiny discontinuities at complex locations. We conducted MTFM detection and TFM image reconstruction experiments for irregular welds containing multiple discontinuities. In an experiment using four 1 mm diameter manufactured defects fabricated on two aluminum alloy welded test blocks, we achieved two significant contributions. First, we accurately detected small discontinuities by combining CIVA simulation with robotic arm assistance. Second, we proposed fusion factor coefficients for TFM image processing, which considered different modal weights for image fusion and de-noising, thereby preserving the integrity of the fused images. Our experimental results demonstrate that the reconstructed TFM images effectively represented all defect information. Compared with other modal TFM images with the highest signal-to-noise ratio (SNR), the amplitude-corrected optimized TFM image exhibits an improved SNR of 51.95% without losing defect information.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140271337","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：