Artificial Intelligence Aproaches as Tools for Auditing and Improving Data Analysis of Advanced Ultrasound Techniques in Non-Destructive Testing

2018 ◽  
Author(s):  
Neil Harrap ◽  
Richard Rheaume ◽  
Andre Gosselin
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

MODELING A THERMAL IMAGING PROCESS IN AN IMPACT DAMAGED COMPOSITE STRUCTURE

2005 ◽  
Vol 02 (01) ◽  
pp. 63-76
Author(s):  
M. Z. ISKANDARANI ◽  
N. F. SHILBAYEH
Keyword(s):  
Data Analysis ◽  
Composite Structure ◽  
Thermal Imaging ◽  
Electronic Circuit ◽  
Impact Damage ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Novel Approach ◽  
Wavelength Variation ◽  
Non Destructive

An innovative NDT (non-destructive testing) technique for interrogating materials for their defects has been developed successfully. The technique has a novel approach to data analysis by employing intensity, RGB signal re-mix and wavelength variation of a thermally generated IR-beam onto the specimen under test which can be sensed and displayed on a computer screen as an image. Specimen inspection and data analysis are carried out through pixel level re-ordering and shelving techniques within a transformed image file using a sequence grouping and regrouping software system, which is specifically developed for this work. The interaction between an impact damaged RIM composite structure and thermal energy is recorded, analyzed, and modeled using an equivalent Electronic circuit. Effect of impact damage on the integrity of the composite structure is also discussed.

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.

A Review of Artificial Intelligence Applications in Thermographic Non-Destructive Testing

2021 ◽  
Author(s):  
Kailun Deng ◽  
Lichao Yang ◽  
Haochen Liu ◽  
Wenhan Li ◽  
John Ahmet Erkoyuncu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

Artificial intelligence for non-destructive testing of CFRP prepreg materials

2019 ◽  
Vol 13 (5) ◽  
pp. 617-626 ◽  
Author(s):  
Carsten Schmidt ◽  
Tristan Hocke ◽  
Berend Denkena
Keyword(s):  
Artificial Intelligence ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

A Review of Residual Life Prediction for Remanufacturing of Machine Tool

2014 ◽  
Vol 552 ◽  
pp. 133-138 ◽  
Author(s):  
Zhen Yu Han ◽  
Guang Нu Liu ◽  
Hong Нa Fu
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Life Prediction ◽  
System Analysis ◽  
Residual Life ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Analysis And Synthesis ◽  
Residual Life Prediction ◽  
Non Destructive

A growing concern about the environment problems, especially about the waste, carbon emissions and landfill, has spurred research into the field of remanufacturing. This paper mainly focuses on the residual life prediction in the remanufacturing of machine tools, which is an important step of remanufacturing process. A system analysis and synthesis is performed in the fields of testing data collection and data analysis and calculation, which are the two important components of residual life prediction. Some non-destructive testing technologies for data collection and some algorithms for data analysis are summarized and made comparison. In addition, this paper also aims at giving a perspective in such area in the future.

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