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.

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.

Nondestructive Testing and Structural Condition Monitoring of Mechanical Cables

1993 ◽  
Vol 46 (4) ◽  
pp. 133-138 ◽  
Author(s):  
Patricio A. A. Laura
Keyword(s):  
Acoustic Emission ◽  
Nondestructive Testing ◽  
Condition Monitoring ◽  
Structural Condition ◽  
Non Destructive Testing ◽  
Acoustic Emission Method ◽  
Testing Methods ◽  
Emission Method ◽  
Destructive Testing ◽  
Non Destructive

This article concerns the problem of evaluating the `structural health’ of cables or ropes by means of non-destructive testing methods. Special emphasis is placed upon electromagnetic techniques and the acoustic emission method.

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.

Data fusion in non destructive testing using fuzzy logic to evaluate friction stir welding

2008 ◽  
Vol 22 (12) ◽  
pp. 826-833 ◽  
Author(s):  
T.G. dos Santos ◽  
B.S. Silva ◽  
P. dos Santos Vilaça ◽  
L. Quintino ◽  
J. M.C. Sousa
Keyword(s):  
Fuzzy Logic ◽  
Friction Stir Welding ◽  
Data Fusion ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Friction Stir ◽  
Non Destructive

scholarly journals A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2598
Author(s):  
Romain Cormerais ◽  
Aroune Duclos ◽  
Guillaume Wasselynck ◽  
Gérard Berthiau ◽  
Roberto Longo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Fusion ◽  
Complex Structure ◽  
Fusion Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Aluminum Block ◽  
Artificial Neural ◽  
Non Destructive

In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.

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.

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.

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