Neural Network Based Processing of Thermal NDE Data for Corrosion Detection

Prabhu, D. R.; Winfree, William P.

doi:10.1007/978-1-4615-2848-7_98

Cited by 22 publications

(9 citation statements)

References 2 publications

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“…Machine learning is more accurate and convenient than human judgment in material analysis for the detection of metal corrosion and asphalt pavement cracking and the determination of concrete strength . Agrawal et al explored various applications in which machine learning methods (such as feature selection and predictive modeling) are used to predict the fatigue strength of steel by studying the relationship among various properties of the alloy and its composition and manufacturing process parameters.…”

Section: Applicationsmentioning

confidence: 99%

Machine learning in materials science

Wei

Chu

Sun

et al. 2019

InfoMat

641

298

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Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.

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Section: Applicationsmentioning

confidence: 99%

Machine learning in materials science

Wei

Chu

Sun

et al. 2019

InfoMat

641

298

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“…In [20], transient thermography raw data was used as the input to train a multi-layer NN, which was then capable of detecting 0.25 and 0.5 mm deep FBHs in a 1.0 mm thick AL sheet. Albendea et al, used pulsed thermography on gas tungsten arc welds of 1.0 mm thick stainless steel sheets containing flaws such as lack of penetration and perforation [21].…”

Section: Thermography Non-destructive Testingmentioning

confidence: 99%

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Atwya

Panoutsos

2020

IEEE Trans. Ind. Inf.

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A systematic computational method to simulate and detect sub-surface flaws, through non-destructive transient thermography, in aluminium sheets and friction stir welded sheets is proposed. The proposed method relies on feature extraction methods and a data-driven machine learning modelling structure. In this work, we propose the use of a multi-layer perceptron feed-forward neural-network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose Thermographic Signal Linear Modelling (TSLM), a hyper-parameterfree feature extraction technique for transient thermography. The new feature extraction and modelling framework was tested with out-of-sample experimental transient thermography data and results show effectiveness in sub-surface flaw detection of up to 2.3 mm deep in aluminium sheets (99.8 % true positive rate, 92.1 % true negative rate) and up to 2.2 mm deep in friction stir welds (97.2 % true positive rate, 87.8 % true negative rate).

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“…ANN technology is widely applied in signal processing, pattern recognition, target tracking, robot control, expert system, combined optimization, and prediction system. ANN is also widely used in infrared nondestructive test [3,4] and in pulse infrared nondestructive test [5,6]. In this paper, ENN was applied in lock-in thermography NDT to determine defect depth.…”

Section: Introductionmentioning

confidence: 99%

Application of Evolutionary Neural Network in Infrared Nondestructive Test

Wang

Dai

Zhu

2013

2013 5th International Conference on Intelligent Networking and Collaborative Systems

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Evolutionary neural network (ENN) was used to quantify the defects in lock-in thermography nondestructive test. The embedded specimens containing the multi-defect information were tested, and time-dependent temperature information measured from pixels of an NIR camera's focalplane array detector imaging the surface specimens provided characteristic parameters for network training. The results show that the error from the network after training was less than 2% and can be referred to in engineering application.

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Neural Network Based Processing of Thermal NDE Data for Corrosion Detection

Cited by 22 publications

References 2 publications

Machine learning in materials science

Machine learning in materials science

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Application of Evolutionary Neural Network in Infrared Nondestructive Test

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