scholarly journals Effects of adjusting dynamic e-modulus to reference values of timber moisture and temperature in classification of small number of samples

2019 ◽  
Vol 71 (2) ◽  
pp. 125-135
Author(s):  
Adriana Bjelanović ◽  
Tomislav Franković ◽  
Ivana Štimac Grandić
Keyword(s):  
Reference Values ◽  
Statistical Significance ◽  
Strong Correlations ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Static Testing ◽  
Regression Parameters ◽  
Dynamic Ultrasound ◽  
Non Destructive

Mathematical dependences are derived for non-destructive testing (NDT) and destructive testing (DT) of three timber sets, each with six beams made of soft and hard structural timber. Very strong correlations were established between elastic moduli (e-moduli) determined by non-destructive testing, from dynamic ultrasound testing with direct propagation and static testing to bending action, and the correlation of e-moduli with bending strengths. The effects of adjustment of NDT results to reference values of moisture and temperature, and statistical significance of regression parameters, were evaluated from the standpoint of use in the initial classification of a small number of samples.

scholarly journals Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

2021 ◽  
Author(s):  
P. Trouvé-Peloux ◽  
B. Abeloos ◽  
A. Ben Fekih ◽  
C. Trottier ◽  
J.-M. Roche
Keyword(s):  
Defect Detection ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Learning Approaches ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Experimental Database ◽  
Out Of Plane ◽  
Non Destructive

Abstract This paper is dedicated to out-of-plane waviness defect detection within composite materials by ultrasonic testing. We present here an in-house experimental database of ultrasonic data built on composite pieces with/without elaborated defects. Using this dataset, we have developed several defect detection methods using the C-scan representation, where the defect is clearly observable. We compare here the defect detection performance of unsupervised, classical machine learning methods and deep learning approaches. In particular, we have investigated the use of semantic segmentation networks that provides a classification of the data at the “pixel level”, hence at each C-scan measure. This technique is used to classify if a defect is detected, and to produce a precise localization of the defect within the material. The results we obtained with the various detection methods are compared, and we discuss the drawbacks and advantages of each method.

A New Fuzzy Geometrical Approach to Classify Defects in Composite Materials

2012 ◽  
Vol 2 (4) ◽  
pp. 12-20
Author(s):  
Matteo Cacciola ◽  
Salvatore Calcagno ◽  
Fabio La Foresta ◽  
Mario Versaci
Keyword(s):  
Point Of View ◽  
Geometrical Approach ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Fuzzy Distance ◽  
Reinforced Polymer ◽  
Heuristic Technique ◽  
Non Destructive

It is well known that in the Non Destructive Testing/Evaluation (NDT/E) context, Ultrasonic Echoes (UEs) and Tests (UTs) are intensively exploited to identify and characterize defects in the Carbon Fiber Reinforced Polymer (CFRP). This paper examines the localization and the classification of defects in this material from a fuzzy geometrical point of view. In particular, starting from an experimental campaign of measurements carried out in our Lab (Laboratory of Electrical Engineering & Non-Destructive Tests and Evaluations, “Mediterranea” University of Reggio Calabria), fuzzy subsethood calculus is taken into account to translate the characterization of a defect in CFRP into a sort of “fuzzy distance” among UEs. Finally, the floor is open for any questions related to the comparison with a higher computational complexity heuristic technique.

scholarly journals A NEW APPROACH TO ASSESSING THE OPERATIONAL SAFETY OF COMPOSITE MATERIALS AND PARTS OF COMPLEX STRUCTURES BASED ON ARTIFICIAL INTELLIGENCE METHODS AS A PART OF NEURAL NETWORKS AND DEEP RESULTS OF MULTI-CRITERIA COMPLEX NON-DESTRUCTIVE TESTING

2020 ◽  
pp. 18-27
Author(s):  
D. A. Akimov ◽  
A. D. Kleymenov ◽  
S. O. Kozelskaya ◽  
O. N. Budadin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Complex Structures ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
New Approach ◽  
Heterogeneous Information ◽  
Operational Safety ◽  
Non Destructive

The article proposes a new approach to assessing the operational safety of materials and parts of complex structures based on artificial intelligence methods based on artificial neural networks and multi-criteria complex non-destructive testing, and special mathematical and algorithmic support for systems for evaluating operational safety and predicting residual life under external influences. A method of morphological analysis of the procedures for using measurement tools for heterogeneous information with different a priori information, both about the type of characteristics and the distribution of errors in the input and output signals, has been developed. The classification of problems of measuring parameters for the integration of heterogeneous information is proposed. A macromodel of error is obtained that can be used for research purposes to minimize errors in the developed equipment or for the purpose of correcting errors during operation. A classification of methods for measuring heterogeneous information from the standpoint of probability distribution theory is proposed. Experimental testing of developed algorithms tailored aggregation of information non-destructive testing and adaptation to poorly formalized parameters, which confirmed the effectiveness of the developed methods and algorithms for assessment of structures and resource forecasting their operational reliability was carried out.

Mechanical Properties of Trabecular Bone by a Non-Destructive Compression Testing Approach

1988 ◽  
Vol 17 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Frank Linde ◽  
Charlotte Buch Gøosthgen ◽  
Ivan Hvid ◽  
Buntoeng Pongsoipetch ◽  
Søosren Bentzen
Keyword(s):  
Trabecular Bone ◽  
Modulus Of Elasticity ◽  
Linear Part ◽  
Quantitative Computed Tomography ◽  
Strong Correlations ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Compression Curve ◽  
Ct Number ◽  
Non Destructive

In order to optimize non-destructive mechanical testing of trabecular bone specimens, different techniques were analysed, and correlations were established between properties derived from such non-destructive testings and those derived from destructive testing. Non-destructive testing to a fixed percentage of predicted ultimate stress was hampered by inaccuracy of this prediction. Simulation of non-destructive testing conducted to the linear' part of the compression curve using a drop of the increase of the stiffness (slope of the compression curve) below a certain value as stop criterion revealed strong correlations ( r: 0.97–0.99) between the stiffness at the stop point and modulus of elasticity derived from destructive testing. However, trabecular damage will probably occur during such testing because high strain values were obtained. Testing to a fixed strain (0.6 per cent) also revealed strong correlation between the stiffness at the 0.6 per cent strain level and modulus of elasticity ( r = 0.96) derived from destructive testing. By this technique trabecular damage was avoided and standardized elastic and viscoelastic energies could be obtained. Prediction of modulus of elasticity by indirect methods such as modulus of elasticity of specimens from the opposite symmetric location ( r = 0.73), bone mineral concentration ( r = 0.72) derived from photon absorptiometry and the CT number ( r = 0.78) derived from quantitative computed tomography showed less strong correlations.

scholarly journals A Study on the Detection of Internal Defect Types for Duct Depth of Prestressed Concrete Structures Using Electromagnetic and Elastic Waves

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3931
Author(s):  
Young-Geun Yoon ◽  
Jae-Yun Lee ◽  
Hajin Choi ◽  
Tae-Keun Oh
Keyword(s):  
Prestressed Concrete ◽  
Elastic Waves ◽  
Principal Component ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Flexural Mode ◽  
Impact Echo ◽  
Multichannel Analysis ◽  
Non Destructive

Prestressed concrete (PSC) is widely used for the construction of bridges. The collapse of several bridges with PSC has been reported, and insufficient grout and tendon corrosion were found inside the ducts of these bridges. Therefore, non-destructive testing (NDT) technology is important for identifying defects inside ducts in PSC structures. Electromagnetic (EM) waves have limited detection of internal defects in ducts due to strong reflections from the surface of the steel ducts. Spectral analysis of the existing impact echo (IE) method is limited to specific conditions. Moreover, the flexural mode in upper defects of ducts located at a shallow depth and delamination defects inside ducts are not considered. In this study, the applicability of the elastic wave of IE was analyzed, and multichannel analysis of surface, EM, and shear waves was employed to evaluate six types of PSC structures. A procedure using EM waves, IE, and principal component analysis (PCA) was proposed for a more accurate classification of defect types inside ducts. The proposed procedure was effective in classifying upper, internal, and delamination defects of ducts under 100 mm in thickness, and it could be utilized up to 200 mm in the case of duct defect limitations.

THE BASIC TRENDS OF DEVELOPMENT OF VIBRODIAGNOSTIC NONDESTRUCTIVE TESTING OF TECHNICAL FACILITIES

2015 ◽  
pp. 100-103
Author(s):  
V. V. Piven ◽  
G. Yu. Gondurov
Keyword(s):  
Nondestructive Testing ◽  
Evolutionary Model ◽  
Actual Condition ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Vibration Diagnostics ◽  
Rotating Equipment ◽  
Non Destructive

The main types of technical facilities non-destructive testing are presented. The classification of defects in rotating equipment by different indicators was made. The basic trends in development of vibrodiagnostic non-destructive testing of technical objects are described. The evolutionary model of vibration diagnostics at transition to service and repair based on the actual condition was developed.

An in-process multi-feature data fusion nondestructive testing approach for wire arc additive manufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xi Chen ◽  
Youheng Fu ◽  
Fanrong Kong ◽  
Runsheng Li ◽  
Yu Xiao ◽  
...  
Keyword(s):  
Temperature Field ◽  
Nondestructive Testing ◽  
Data Fusion ◽  
Slag Inclusion ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Content Type ◽  
Process Detection ◽  
Non Destructive

Purpose The major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that are time-consuming, expensive and destructive. This paper aims to achieve the on-line detection and classification of the common defects, including hump, deposition collapse, deviation, internal pore and surface slag inclusion. Design/methodology/approach This paper proposes an in-process multi-feature data fusion nondestructive testing method based on the temperature field of the WAAM process. A thermal imager is used to collect the temperature data of the deposition layer in real-time. Efficient processing methods are proposed in this paper, such as the temperature stack algorithm, width extraction algorithm and a classification model based on a residual neural network. Some features closely related to the forming quality were extracted, containing the profile image and width curve of the deposition layer and abnormal temperature features in longitudinal and cross-sections. These features are used to achieve the detection and classification of defects. Findings Thermal non-destructive testing is a potentially superior technology for in-process detection in the industrial field. Based on the temperature field, extracting the most relevant features of the defect information is crucial. This paper pushes current infrared (IR) monitoring methods toward real-time detection and proposes an in-process multi-feature data fusion non-destructive testing method based on the temperature field of the WAAM process. Originality/value In this paper, the single-layer and multi-layer WAAM samples are preset with various defects, such as hump, deposition collapse, deviation, pore and slag inclusion. A multi-feature nondestructive testing methodology is proposed to realize the in-process detection and classification of the defects. A temperature stack algorithm is proposed, which improves the detection accuracy of profile change and solves the problem of uneven temperature from arc striking to arc extinguishing. The combination of residual neural network greatly improves the accuracy and efficiency of detection.

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