Defect-identification in composite materials using pattern recognition techniques on ultrasonic data

Schillemans, L.; Hemelrijck, Danny Van; Roey, F. De; Wilde, W. P. De; Cardon, Albert; Anastassopoulos, A. A.

doi:10.1163/156855193x00070

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…In composite materials, AI has been employed for diagnostics and prognostics by using supervised learning meth- ods such as linear models [45][46][47][48], multivariate regression [49], support vector machine (SVM) [47,48,[50][51][52][53][54][55][56], decision trees, boosting and random forests [47,48,57-59], K-nearest algorithms [46,55], polynomial classifiers (PCs) [60, 61]; unsupervised learning methods such as pattern recognition and clustering algorithms [62-66], principal component analysis (PCA) [67][68][69][70], K-means [71][72][73], fuzzy C-means [71,74,75], Kohonen self organizing maps (KSOMs) [68,72,76], parameter correction techniques (PCTs) [77]; and also several combinations of unsupervised with supervised learning as a hybrid ML algorithms [78][79][80][81][82][83][84][85]. Also, artificial neural networks (ANNs) and its modified types [58,76, have been typically employed to model the damage and predict strength and life.…”

Section: Ai Paradigms In Compositesmentioning

confidence: 99%

“…Schillemans et al used pattern recognition algorithms such as K-nearest, potential function classifier, linear classifier, F-machine, and modified K-nearest in identifying the defects of foreign bodies like aluminum and Teflon in CFRP using the time domain PZT signals. Classifying error is lower order than classifying in random, hence pattern recognition reduces the manual human work and has great potential [46].…”

Section: Piezoelectric Guided Wave Sensingmentioning

confidence: 99%

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Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Elenchezhian

Vadlamudi

Raihan

et al. 2021

Smart Mater. Struct.

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In the era of the 4th industrial revolution of big data, artificial intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous MS. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and prognostics health management of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for diagnosis, prognosis, and control.

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Section: Ai Paradigms In Compositesmentioning

confidence: 99%

Section: Piezoelectric Guided Wave Sensingmentioning

confidence: 99%

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Elenchezhian

Vadlamudi

Raihan

et al. 2021

Smart Mater. Struct.

View full text Add to dashboard Cite

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“…Feature extraction for various ultrasonic inspection applications has been well covered in the literature. Ultrasonic data have been described in the time domain; 17,[23][24][25][26][27] frequency domain; 17,[23][24][25][26][27][28] correlation, convolution and deconvolution domains; 29 and with wavelets. 23,26,[30][31][32] Making use of all of the features presented in the literature would lead to a large scale of redundant information and very slow computation times, and so a narrow selection was adopted based on the review by Lee.…”

Section: Feature Extractionmentioning

confidence: 99%

Defect characterisation in laminar composite structures using ultrasonic techniques and artificial neural networks

Barry

Kesharaju

Nagarajah

et al. 2015

Journal of Composite Materials

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This paper presents an approach to defect detection and characterisation in ultrasonic inspection of laminate composite panels. A set of features coupled with gates was identified along with a method for sub-dividing and thresholding the ultrasonic data, which removes most of the location specific information from the defect data thus increasing the generalisation capabilities of the defect classifier. Validation results obtained from independent defect data indicate that the performance of the presented data description tools coupled with an artificial neural network classifier was able to correctly locate and classify defects at different depths. It was concluded that a structured approach to the pre-processing of ultrasonic testing data, combined with the selective feature extraction for artificial neural network classification, greatly reduces the requirement for artificial neural network training data. Furthermore, it allows for improved performance across a variety of panel geometries.

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Demonstration of using signal feature extraction and deep learning neural networks with ultrasonic data for detecting challenging discontinuities in composite panels

Aldrin¹,

Forsyth

2019

AIP Conference Proceedings

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Defect-identification in composite materials using pattern recognition techniques on ultrasonic data

Cited by 4 publications

References 5 publications

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Defect characterisation in laminar composite structures using ultrasonic techniques and artificial neural networks

Demonstration of using signal feature extraction and deep learning neural networks with ultrasonic data for detecting challenging discontinuities in composite panels

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