scholarly journals Standardization of the new flaw detection material for magnetic powder method non-destructive testing

2020 ◽  
Vol 986 ◽  
pp. 012006
Author(s):  
A. A. Chesnokova ◽  
V. A. Ivanova ◽  
S. Z. Kalaeva
Keyword(s):  
Flaw Detection ◽  
Magnetic Powder ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Powder Method ◽  
Non Destructive

scholarly journals Technical and metrological aspects of non-destructive tests of selected elements of rope transport installations

2017 ◽  
Vol 2017 (3) ◽  
pp. 53-64
Author(s):  
Jerzy Kwaśniewski ◽  
Tomasz Krakowski ◽  
Szymon Molski ◽  
Hubert Ruta ◽  
Jakub Szybowski
Keyword(s):  
Magnetic Powder ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive ◽  
Limited Sensitivity

The paper highlights the engineering and metrological aspects of non-destructive testing of selected components of cableway installations and ski-lifts. The example of clamping systems in cableaways is recalled and the main point raised is whether the areas subjected to highest stress could be identified by the FEM approach. Other issues include the aspects involved in removal of the outer layers of the materials to account for possible increment or decrement of readouts, in accordance with the standard PN-EN 10228-1 and limited sensitivity of magnetic powder tests done through anti-corrosion coating.

scholarly journals Augmented Ultrasonic Data for Machine Learning

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Iikka Virkkunen ◽  
Tuomas Koskinen ◽  
Oskari Jessen-Juhler ◽  
Jari Rinta-aho
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Flaw Detection ◽  
Machine Learning Algorithms ◽  
Non Destructive Testing ◽  
Classification Problems ◽  
Destructive Testing ◽  
Complex Signals ◽  
Convolutional Network ◽  
Non Destructive

AbstractFlaw detection in non-destructive testing, especially for complex signals like ultrasonic data, has thus far relied heavily on the expertise and judgement of trained human inspectors. While automated systems have been used for a long time, these have mostly been limited to using simple decision automation, such as signal amplitude threshold. The recent advances in various machine learning algorithms have solved many similarly difficult classification problems, that have previously been considered intractable. For non-destructive testing, encouraging results have already been reported in the open literature, but the use of machine learning is still very limited in NDT applications in the field. Key issue hindering their use, is the limited availability of representative flawed data-sets to be used for training. In the present paper, we develop modern, deep convolutional network to detect flaws from phased-array ultrasonic data. We make extensive use of data augmentation to enhance the initially limited raw data and to aid learning. The data augmentation utilizes virtual flaws—a technique, that has successfully been used in training human inspectors and is soon to be used in nuclear inspection qualification. The results from the machine learning classifier are compared to human performance. We show, that using sophisticated data augmentation, modern deep learning networks can be trained to achieve human-level performance.

scholarly journals IMPROVING THE CAPILLARY METHOD OF NON-DESTRUCTIVE TESTING

2019 ◽  
pp. 33-38
Author(s):  
V. V. Lopatin
Keyword(s):  
Quality Control ◽  
Aerospace Industry ◽  
Flaw Detection ◽  
Non Destructive Testing ◽  
Labor Costs ◽  
Destructive Testing ◽  
Wetting Ability ◽  
Capillary Method ◽  
The Cost ◽  
Non Destructive

The history of capillary control began in the 40s of the last century for the needs of the aerospace industry. Currently, the cost of quality control in the aerospace industry is up to 12 - 18% of the cost of products. Similar amounts of expenses in the nuclear and defense industries are not lagging behind other industries. For example, for the control of welded joints of oil and gas pipelines of large diameter and considerable length, the labor costs for inspection reach 10% of the total labor costs. Capillary quality control method is based on the ability of indicator liquids (penetrants) to penetrate into the cavities of surface defects (discontinuities). Over the 70 years of its existence, the capillary method of control has not undergone fundamental changes, and its principles have remained unchanged. In international practice, the abbreviated designation of types of non-destructive testing (AWS) is adopted, and the control with the use of penetrating liquid denoted RT. This method is applicable to the detection of all types of surfacedead-end and through defects, such as cracks, delamination, leaks, in products made from any non-porous materials, including glass, ceramics, plastics and other non-metallic materials. The analysis of the capillary method of nondestructive testing of the surface of a solid body is carried out, the possibilities and ways of its improvement are indicated. The method of the capillary method of non-destructive testing of a solid surface, the physics of the method and its implementation are considered in detail. It is shown that the wetting ability and spreading are important characteristics of capillary control fluids; therefore, they must be evaluated and analyzed when developing new ones, choosing or comparing known capillary flaw detection materials. The possibility of using the Rebinder effect to improve the capillary method of non-destructive testing of a solid surface has been proved. A refined method of capillary defectoscopy is proposed by taking into account the wetting ability, density, viscosity and evaporation of a liquid, which makes it possible to make an optimal choice of liquid to ensure high efficiency of surface (capillary)control. An improved method for assessing the wetting ability of liquids is proposed, which makes it possible to evaluate the wetting ability of liquids by the size of the spreading spot of their droplets, taking into account the influence of density, viscosity and evaporation of liquids intended for capillary flaw detection (penetrants).

COMPARISON OF RUSSIAN AND INTERNATIONAL STANDARDS WHEN CONDUCTING THE MAGNETIC POWDER METHOD OF NON-DESTRUCTIVE TESTING

2021 ◽  
pp. 117-123
Author(s):  
I.S. Lednev ◽  
◽  
A.S. Generalov ◽  
Keyword(s):  
Magnetic Field ◽  
Remanent Magnetization ◽  
Magnetic Particle ◽  
International Standards ◽  
Magnetic Powder ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Advantages And Disadvantages ◽  
Non Destructive ◽  
Magnetic Particle Testing

The analysis of the requirements of domestic and foreign standards for the magnetic particle testing (MPT) method is presented. Their advantages and disadvantages are highlighted, as well as fundamental differences in the technology of control, of which the main one is the use of the remanent magnetization method in the practice of domestic MPT. The methods of remanent magnetization and the applied magnetic field are compared. Based on the analysis, it was concluded that for the control of particularly responsible products during the MPT, GOST 56512-2015 should be followed.

Productivity and Reliability Study of Non Destructive Testing Techniques for Inspection of Structural Welds in Construction Industry

NDT World ◽  
2016 ◽  
Vol 19 (3) ◽  
pp. 35-39
Author(s):  
Чан ◽  
Alan Chan ◽  
Бабу ◽  
Sajeesh Kumar Babu ◽  
Чан ◽  
...  
Keyword(s):  
Construction Industry ◽  
Ultrasonic Testing ◽  
Phased Array ◽  
Flaw Detection ◽  
Slag Inclusion ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Ultrasonic Techniques ◽  
Testing Techniques ◽  
Non Destructive

Introduction. The aim of this study is to evaluate the productivity and reliability of non-destructive testing techniques for the inspection of structural welds employed in the Hong Kong construction industry. Method. Manual ultrasonic pulse echo method and semi-automatic ultrasonic techniques using phased array (PAUT) as well as radiographic testing were employed. Five classes of defects were analyzed: lack of penetration, lack of fusion, crack, porosity and slag inclusion. The tests were carried out on the specimen made from structural plate, on which artefacts were inserted on the weld metal. The results were being studied to compare the defect detection reliability by both ultrasonic techniques. The flaw detection productivity using phased array is also compared with conventional ultrasonic testing at a determined rate. Results. The reliability of PAUT was 100% compared to 96.7% with manual ultrasonic testing, however with the inclusion of defect sizing and tolerance the reliability of manual UT is dropped to 57.4%, which implies there is a chance of 42.6% of improper sizing). PAUT exhibits the reliability of 87.5%. The research will be continued with the aim of determining the most appropriate and reliable NDT methods in each case.

scholarly journals The analysis of methods effectiveness of automated non-destructive testing of products based on Data Mining methods

2018 ◽  
Vol 224 ◽  
pp. 02062
Author(s):  
Maksim V. Ovechkin ◽  
Eugeniy S. Shelihov ◽  
Julia I. Ovechkina
Keyword(s):  
Data Mining ◽  
Flaw Detection ◽  
Distance Matrix ◽  
Short Wave ◽  
Training Data ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Automated Control ◽  
Non Destructive ◽  
Self Organizing

Purpose of the study: the analysis of the effectiveness of automated nondestructive testing methods within the objectives of data clustering on the use of short-wave electromagnetic radiation in flaw detection. Research methods: Kohonen self-organizing maps (SOM). The relevance of the work is that due to the increased demand for quality and reliability of products are becoming increasingly important physical methods for automated control of metals and products thereof that do not require cutting or fracture specimens of finished productes. The article noted common features of methods of short-wave electromagnetic control of products. The effectiveness of the Data Mining approach to the construction of a hypothesis on the interrelationships of data groups on non-destructive testing of products is substantiated. As an instrument, the method of self-organizing Kohonen maps was chosen. An example of a part of training data and neural network configuration parameters performing the task of visualization and clustering is given. It is concluded about the lead electromagnetic methods of automated control of complex products in production. The resulting distance matrix and the cluster map are shown. An example of applying the results of analysis to the problem of testing spot welded joints is considered. Given the further directions of research is to develop a computer image processing techniques in the framework of automated non-destructive testing systems.

Reduced False Calls in Eddy Current Images Using Signal Processing

2021 ◽  
Author(s):  
S. B. Mahalakshmi ◽  
Ganesh Seshadri ◽  
Aparna Sheila-Vadde ◽  
Manoj Kumar KM
Keyword(s):  
Eddy Current ◽  
Flaw Detection ◽  
Non Destructive Testing ◽  
Testing Methods ◽  
Destructive Testing ◽  
Noise Measurements ◽  
Component Manufacturing ◽  
Lift Off ◽  
The Time Domain ◽  
Non Destructive

Abstract Non-destructive testing methods are used largely in component manufacturing industries like Aerospace, Renewables and Power to evaluate the properties of a material or the quality of a component by inspecting for cracks and discontinuities without causing damage to the part. Among the many non-destructive testing methods, Eddy current imaging enables efficient flaw detection for surface and sub-surface cracks. However, in typical eddy current inspection, there can be significant number of false calls arising from variation in lift-off and surface anomalies. Discriminating defect signals from false calls can be very challenging. This paper describes a method to reduce false calls by using a wavelet based denoising algorithm and combining it with statistical-based features extracted inside a sliding window in the time domain to efficiently identify the cracks. The results are verified on specimens with cracks of different sizes that are oriented randomly along with locations available for baseline noise measurements.

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