Research in Nondestructive Evaluation最新文献

{"title":"Evaluation of Defect Depth of X70 Pipeline Steel Based on the Piezomagnetic Signals","authors":"Sheng Bao, Pengfei Jin","doi":"10.1080/09349847.2023.2283512","DOIUrl":"https://doi.org/10.1080/09349847.2023.2283512","url":null,"abstract":"In this research, the correlation between the defect depth and the tangential piezomagnetic field of X70 steel was investigated. Tensile tests were carried out to measure the piezomagnetic fields o...","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"60 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138525752","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}

{"title":"Comparison of In-situ Nondestructive Testing and Ex-situ Methods in Additive Manufactured Specimens for Internal Feature Detection","authors":"Youssef AbouelNour, Nikhil Gupta","doi":"10.1080/09349847.2023.2280650","DOIUrl":"https://doi.org/10.1080/09349847.2023.2280650","url":null,"abstract":"The effectiveness and repeatability of Additive Manufacturing (AM) technologies has been often measured through rigorous testing, both in-situ and ex-situ. In-situ nondestructive testing (NDT) has ...","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"69 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138525720","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}

{"title":"Utilizing an Ultrasonic Inspection System Operating Inside an Autoclave and Machine Learning to Quantify Porosity within Composites During Cure","authors":"Tyler B. Hudson, Gavin R. Chung, Joseph J. Pinakidis, Patrick J. Follis, Thammaia Sreekantamurthy, Frank L. Palmieri","doi":"10.1080/09349847.2023.2277424","DOIUrl":"https://doi.org/10.1080/09349847.2023.2277424","url":null,"abstract":"ABSTRACTComposite materials are increasingly being utilized in aerospace applications for their high stiffness and strength to weight ratios and fatigue resistance. However, defects in the composite may arise during cure (e.g. porosity, delamination, fiber waviness), and current technology only allows for post-cure evaluation (e.g. microscopy, ultrasonic inspection). A high-temperature ultrasonic scanning system was developed for deployment in an autoclave, which can detect porosity in composites during the cure process. This study focused on the implementation of machine learning techniques to help generate a model that can quantify porosity, in addition to detection and localization that has previously been demonstrated. Two, six-hour-long experiments were conducted on curing of 762 mm × 305 mm (30 in. ×12 in.) composite panels with a [0/45/90/-45]4s layup and varying regions of high and low pressure due to its tapered geometry in contact with a flat caul plate. The first experiment utilized a thick (12.7 mm) caul plate and the second utilized a thin (3.2 mm) caul plate. During experimentation, within the scan area (406 mm × 13 mm), data was recorded and stored for ultrasonic amplitude. Additional variables were measured or predicted including temperature, autoclave pressure, number of plies, slope of the composite panel surface with respect to the transducer, viscosity, and glass transition temperature. The pre-processed data was entered into the Regression Learner Application in MATLABⓇ,Footnote1 and a rational quadratic Gaussian process regression (GPR) was chosen for the machine learning algorithm. The model was then trained on a larger data set to make it more robust and capable of predictions using a function callout. The result was a machine learning algorithm that can reliably quantify porosity in a composite panel during cure based on measured amplitude response and generate images for intuitive visualization. This tool can be further trained with more experimentation and potentially employed for real-time porosity detection and quantification of composite components during cure in an autoclave. Practical use of this technology is the potential to dynamically control processing parameters (e.g. autoclave pressure) in real-time to reduce the level of porosity within the laminate to acceptable limits (e.g. 2% by volume).KEYWORDS: Defect DetectionInspection During CureMachine Learning (ML)PorosityProcess MonitoringUltrasonic Testing (UT) AcknowledgmentsThe authors would like to acknowledge Hoa Luong and Sean Britton for their contributions to the experimental setup and data collection. Research reported in this publication was supported by funding provided by the Aeronautics Research Mission Directorate (ARMD) of the National Aeronautics and Space Administration (NASA).Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. Specific vendor and manufacturer names are explicitly mentioned only to accurate","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"57 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348300","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}

{"title":"Detection of Barely Visible Impact Damage in Composite Structures Using Backward Sweep Vibro-thermography Technique Utilizing Asymmetry in Local Defect Resonance","authors":"Manish Sharma, Tanmoy Bose","doi":"10.1080/09349847.2023.2269122","DOIUrl":"https://doi.org/10.1080/09349847.2023.2269122","url":null,"abstract":"ABSTRACTIn this article, local defect resonance-based vibrothermography has been studied for different sweep direction and ranges. Two different carbon fiber-reinforced polymer (CFRP) composite plate has been fabricated from vacuum assisted resin transfer molding. In the first plate, a flat bottom hole has been made and two barely visible impact damages are created in other plate. The area of delamination has been determined from phased array ultrasound testing. Laser doppler vibrometry (LDV) has been performed first for different sweep ranges and directions. The CFRP plate is excited with a piezoelectric element at 150 Vpp and the vibration over defect area is captured using a single point laser doppler vibrometer, operating in scanning mode. It has been found that vibration amplitude at local defect resonance (LDR) frequency increases in narrow sweep range compared to a wideband excitation. Again, in both cases, it has been found that backward sweep produces more amplitude compared to forward one due to softening nonlinearity. An asymmetry in LDR frequency is also been observed when the sweep range is further narrowed. An uncooled microbolometer camera is used for reception in case of vibrothermography. Backward sweep is found to be more effective as compared to the forward one and the temperature increment increases in case of narrowband excitation range.KEYWORDS: Local defect resonance (LDR) asymmetryvibro-thermographylaser doppler vibrometryflat bottom hole (FBH)barely visible impact damage (BVID)carbon fiber reinforced polymer (CFRP) composite AcknowledgmentsThe corresponding author acknowledges Science and Engineering Research Board under Department of Science of Technology, India for funding this work vide grant no. CRG/2019/005045.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Science and Engineering Research Board [CRG/2019/005045].","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779699","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}

{"title":"Application of Reciprocity for Facilitation of Wave Field Visualization and Defect Detection","authors":"Bernd Köhler, Kanta Takahashi, Kazuyuki Nakahata","doi":"10.1080/09349847.2023.2261878","DOIUrl":"https://doi.org/10.1080/09349847.2023.2261878","url":null,"abstract":"ABSTRACTThe motion visualization in a structural component was studied for defect detection. Elastic motions were excited by hammer impacts at multiple points and received by an accelerometer at a fixed point. Reciprocity in elastodynamics is only valid under certain conditions. Its validity under given experimental conditions was derived from the elastodynamic reciprocity theorem. Based on this, the dynamic motion of the structural component was obtained for fixed-point excitation from measurements performed using multipoint excitations. In the visualized eigenmodes, significant additional deformation was observed at the wall thinning inserted as an artificial defect. To prevent the dependence of defect detection on its position within the mode shape, another approach was proposed based on the extraction of guided wave modes immediately after impact excitation. It is shown that this maximum intensity projection method works well in detecting defects.KEYWORDS: Elastodynamic reciprocityvisualization of damagemultisite excitationselective guided wave mode AcknowledgmentsWe gratefully acknowledge the funding support by JSPS.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Japan Society for the Promotion of Science [15K01226] and the Invitational Fellowships for Research in Japan [S20084].","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591788","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}

{"title":"Hands-On Training of Non-destructive Testing Using a Mock-Up in the Curriculum of Civil Engineers","authors":"Sylvia Kessler, Christian U. Grosse","doi":"10.1080/09349847.2023.2255538","DOIUrl":"https://doi.org/10.1080/09349847.2023.2255538","url":null,"abstract":"ABSTRACTOur infrastructure deteriorates progressively and the knowledge about the current condition is crucial to enable proper maintenance. Nondestructive techniques provide the basis for reliable condition assessment and thus, requires talented engineers with corresponding skills. Fortunately, in the curriculum of civil engineers, nondestructive testing gains more and more importance. The challenge in teaching nondestructive testing is to fulfil the requirement that students achieve the learning objective of “Application.” The term “Application” describes the ability that students are able to use nondestructive techniques appropriately. This teaching objective is not achievable in classroom lectures. Thus, the authors developed a mock-up for hands-on learning where students can try several nondestructive techniques such as half-cell potential measurement, Radar, ultrasound, impact-echo etc. Civil engineering students often encounter difficulty with the handling of sometimes very sophisticated devices. The challenge increases even more when the students have to extract the measured data, evaluate them, and relate their results to the condition of the tested object. With the support of the mock-up the authors intended to assist civil engineering students to understand the application of nondestructive techniques. This paper presents the design of the mock-up in combination with the corresponding teaching concept and the first teaching experience.KEYWORDS: Mock-upeducationreinforced concreteNDThalf-cell potential measurementradarultrasoundimpact-echoteaching concept AcknowledgmentsThe authors acknowledge the financial support received from the education fund of the Technical University of Munich. The authors also acknowledge assistance from colleagues, laboratory staff, and students from the center of building materials at different stages of the mock-up from the planning until the execution of the measurement, here in alphabetical order: Robin Groschup, Sebastian Lucka, Fabian Malm, Florian Mrowietz, Manuel Raith, and Alejandro Ramirez Pinto [Citation17].Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136263172","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}

{"title":"Pulsed Eddy Current Testing Method for Corrosion Thinning Defects of Ferromagnetic Components Based on Parameter Inversion Algorithm","authors":"Wensheng Yao, Yufeng Ye, Fenghuai Wang, Haoping Xie","doi":"10.1080/09349847.2023.2236070","DOIUrl":"https://doi.org/10.1080/09349847.2023.2236070","url":null,"abstract":"ABSTRACT Based on the time domain analytical expression of the pulsed eddy current field of a ferromagnetic flat plate, the least square parameter inversion problem between the measured value of the time-domain induced voltage and the theoretical value is established. Set the conductivity of the tested component as a fixed constant and then compare the inversion results of the wall thickness of the two test points to obtain the relative change of the wall thickness between the two test points. The rectangular groove defect is machined on the steel plate, and the experiment verifies that the relative wall thickness detection method in this paper can eliminate the influence of the set value of component conductivity. When the area of the corrosion area is larger than the area of the foot of the probe, the residual wall thickness of the corrosion area can be accurately scanned, and the edge of the corrosion area has good recognition ability. When the area of the thinning area is smaller than the area of the probe foot, the thinning degree of local corrosion defects will be underestimated. The pulsed eddy current method is used to detect the relative wall thickness of ferromagnetic components without contacting with the tested components. The detection results are intuitive and the repeatability is high. It can be used for nondestructive testing and evaluation of the corrosion thinning of the wall thickness of ferromagnetic components with coating in industry.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"7 1","pages":"186 - 204"},"PeriodicalIF":1.4,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78477314","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}

{"title":"Modeling of Eddy Current NDT Simulations by Kriging Surrogate Model","authors":"Yang Bao","doi":"10.1080/09349847.2023.2250281","DOIUrl":"https://doi.org/10.1080/09349847.2023.2250281","url":null,"abstract":"ABSTRACT In this article, the Kriging surrogate model is proposed to accelerate the model-assisted probability of detection (MAPoD) analysis for eddy current nondestructive testing (NDT). The Kriging surrogate model is the key for enabling the efficient MAPoD analysis, considering huge number of uncertainties propagate in the eddy current NDT system, by replacing the time-consuming physical model. To generate the model responses of NDT system, a precise physical model based on the adaptive cross approximation algorithm accelerated boundary element method (BEM) is applied. In the numerical case, the MAPoD study of eddy current for detecting surface flaws in the conducting plate is analyzed. By comparing the PoD metrics achieved by the pure physical model and by the Kriging surrogate model, the accuracy and efficiency of the proposed surrogate model are demonstrated.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"33 1","pages":"154 - 168"},"PeriodicalIF":1.4,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89782671","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}

{"title":"Machine Learning for the Nondestructive Prediction of Neutral Temperature in Continuous Welded Rails","authors":"Matthew Belding, A. Enshaeian, Charles A Hager, P. Rizzo","doi":"10.1080/09349847.2023.2237446","DOIUrl":"https://doi.org/10.1080/09349847.2023.2237446","url":null,"abstract":"ABSTRACT This paper describes the application of machine learning (ML) in the framework of a data-driven nondestructive evaluation (NDE) method to estimate the rail neutral temperature (RNT) of continuous welded rails (CWR). The method consists of triggering vibration of the rail of interest and extracting the power spectral densities (PSDs) of the accelerations associated with the lowest modes of vibration. The PSDs then become the input of an ML algorithm trained to associate the PSD to longitudinal stress and then RNT. In the study presented in this article, the proposed NDE method was tested on a tangent track on wood cross-ties. Vibrations were induced with a hammer and detected with several wireless and wired accelerometers. The PSDs across the 0–700 Hz range were extracted from the time-series. These densities in both the lateral and vertical directions constituted part of the input of an artificial neural network trained and tested with experimental data. The predicted neutral temperatures showed very good agreement with the RNT estimated by an independent party and based on conventional strain-gage rosettes.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"39 1","pages":"121 - 135"},"PeriodicalIF":1.4,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81750045","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}

{"title":"A Generalized Classification Framework with Simultaneous Feature Weighting and Selection Using Antlion Optimization Algorithm","authors":"Manju Mohan, M. M. Ramya","doi":"10.1080/09349847.2023.2236066","DOIUrl":"https://doi.org/10.1080/09349847.2023.2236066","url":null,"abstract":"ABSTRACT The use of machine-learning based algorithms on a large-scale nondestructive evaluation (NDE) data considerably advances the NDE techniques toward complete industrial automation. In this article, simultaneous feature selection and feature weighting are carried out on the magnetic Barkhausen emission (MBE) dataset to demonstrate the significance of optimization in NDE data. Antlion optimization is employed as a searching method to determine the optimum feature set that will maximize the classification performance. The proposed framework is validated for different magnetization frequencies separately and found to be frequency independent. The framework resulted in the selection of four significant features extracted from the MBE response thereby reducing the computational effort and improving the accuracy to 98.4% for AdaBoost classifier. The developed machine learning methodology is a potential strategy for processing industrial sensory data since material testing, property prediction, and categorization are frequent tasks in manufacturing and production engineering industries. Further, this research demonstrated the necessity of embedded intelligence in automation of NDE toward Industrial Revolution 4.0.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"21 1","pages":"102 - 120"},"PeriodicalIF":1.4,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89074023","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}