Classification of ultrasonic signals of thermally aged cast austenitic stainless steel (CASS) using machine learning (ML) models
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초록
Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. Themain drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operatingenvironment. Even though ultrasonic non-destructive testing has been conducted in major nuclearcomponents and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonicsignals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonictechnique (UT) and machine learning (ML) models. Several different ML models, specifically the Knearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models,were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identifiedthat the ML models can predict the category of ultrasonic signals effectively according to the agingcondition
Abstract
Cast austenitic stainless steels (CASSs) are widely used as structural materials in the nuclear industry. Themain drawback of CASSs is the reduction in fracture toughness due to long-term exposure to operatingenvironment. Even though ultrasonic non-destructive testing has been conducted in major nuclearcomponents and pipes, the detection of cracks is difficult due to the scattering and attenuation of ultrasonic waves by the coarse grains and the inhomogeneity of CASS materials. In this study, the ultrasonicsignals measured in thermally aged CASS were discriminated for the first time with the simple ultrasonictechnique (UT) and machine learning (ML) models. Several different ML models, specifically the Knearest neighbors (KNN), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models,were used to classify the ultrasonic signals as thermal aging condition of CASS specimens. We identifiedthat the ML models can predict the category of ultrasonic signals effectively according to the agingcondition
참고문헌 (31)
- Initial assessment of the mechanisms and significance of low-temperature embrittlement of cast stainless steels in LWR system, "O.K. Chopra", Nuclear Regulatory Commission, ~, pp.NUREG (1990), CR-5385
- H. M. Chung, "Aging and life prediction of cast duplex stainless steel components", Int. J. Pres. Ves. Pip, 50(1-3), pp.179~213 (1992)
- H. Jang, "The effects of reversion heat treatment on the recovery of thermal aging embrittlement of CF8M cast stainless steels", Mater. Des, 56, pp.517~521 (2014)
- C. Jang, "Evaluation of the recovery of thermal aging embrittlement of CF8M cast stainless steels after reversion heat treatments", Int. J. Pres. Ves. Pip, 131, pp.67~74 (2015)
- T. S. Byun, "Degradation of impact toughness in cast stainless steels during long-term thermal aging", J. Nucl. Mater, 542, pp.152524~ (2020)
- B. S. Kong, "Evaluation of thermal ageing of cast austenitic stainless steels e mechanical properties in micro- and macro-scale", Proceedings of the 13th International Symposium on the Integrity of Nuclear Components, Virtual Meeting (2021)
- C. Wunderlich, "Advanced methods in NDE using machine learning approaches", AIP Conference Proceedings (2018)
- J.B. Harley, "Machine learning and NDE: past, present, and future", Conference Proceedings (2019)
- P. Gardner, "Machine learning at the interface of structural health monitoring and nondestructive evaluation", Philosophical Transactions of the Royal Society A, 378(2182), pp.20190581~ (2020)
- A. J. Fredo, "Classification of damages in composite images using Zernike moments and support vector machines", Compos. B Eng, 168, pp.77~86 (2019)
- W. Choi, "A neural network model for material degradation detection and diagnosis using microscopic images", IEEE Access, 7, pp.92151~92160 (2019)
- Nauman Munir, "Investigation of deep neural network with drop out for ultrasonic flaw classification in weldments", Journal of Mechanical Science and Technology, 32(7), pp.3073~3080 (2018), 대한기계학회
- N. Munir, "Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder", NDT E Int, 111, pp.102218~ (2020)
- P. Jeong, "Ultrasonic examination of cast stainless steel component in nuclear power plant", Review of Progress in Quantitative Nondestructive Evaluation, pp.2105~2112 (1989)
- Ultrasonic Characterization of Cast Austenitic Stainless Steel Microstructure: Discrimination between Equiaxed-And Columnar-Grain MaterialeAn Interim Study, "P. Ramuhalli", Pacific Northwest National Lab.(PNNL), ~, pp.PNNL-18912 (2009)
- NDE Reliability Issues for the Examination of CASS Components, "R.E. Jacob", Pacific Northwest National Lab, ~, pp.PNNL-28840 (2019)
- P. Majumdar, "Elastic modulus of biomedical titanium alloys by nano-indentation and ultrasonic techniques e a comparative study", Mater. Sci. Eng, 489, pp.419~425 (2008)
- 김종범, "Applicability of nonlinear ultrasonic technique to evaluation of thermally aged CF8M cast stainless steel", Nuclear Engineering and Technology, 52(3), pp.621~625 (2020), 한국원자력학회
- "ASTM A351", A351M-00, Standard Specification for Castings, Austenitic, Austenitic-Ferritic (Duplex), for Pressure-Containing Parts, 2000
- L. Buitinck, "API Design for Machine Learning Software: Experiences from the Scikit-Learn Project", arXiv, ~ (2015)
- L. E. Peterson, "K-nearest neighbor", Scholarpedia, 4(2), pp.1883~ (2009)
- W. S. Noble, "What is a support vector machine?", Nat. Biotechnol, 24(12), pp.1565~1567 (2006)
- "Scikit-learn (support vector machine)"[URL]
- M. W. Gardner, "Artificial neural networks(the multilayer perceptron)-a review of applications in the atmospheric sciences", Atmospheric environment, 32(14-15), pp.2627~2636 (1998)
- V. Nair, "Rectified linear units improve restricted Boltzmann machines", ~ (2010)
- "Adam: A Method for Stochastic Optimization"[URL]
- F. Pedregosa, "Scikit-learn : machine learning in Python", J. Mach. Learn. Res, 12, pp.2825~2830 (2011)
- "DeepAi"[URL]
- G. Zhang, "Artificial neural networks in bankruptcy prediction : general framework and cross-validation analysis", Eur. J. Oper. Res, 116(1), pp.16~32 (1999)
- "Scikit-learn (Confusion matrix)"[URL]
- C. Goutte, "A probabilistic interpretation of precision, recall and Fscore, with implication for evaluation", European Conference on Information Retrieval, pp.345~359 (2005)