Deep Neural Network Modeling for Metallic Component Defects Using the Finite Element Model

Isamail, Liyana; Maskuri, Nor Liyana; Isip, Neil Jeremy; Lokman, Siti Farhana; Bakar, Muhamad Husaini Abu

doi:10.1007/978-3-030-28505-0_23

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Although DL has been used in UT in many studies for the detection of cracks [36,37], corrosion [38], welding defects [39,40], and others [31,[33][34][35]41], only a few studies on DLbased UT under low SNR conditions have been reported. For example, Munir et al [34] used DL for the classification of welding defects at low SNRs.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Ultrasonic Testing to Evaluate the Porosity of Additively Manufactured Parts with Rough Surfaces

Park

Hong

et al. 2021

Metals

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Ultrasonic testing (UT) has been actively studied to evaluate the porosity of additively manufactured parts. Currently, ultrasonic measurements of as-deposited parts with a rough surface remain problematic because the surface lowers the signal-to-noise ratio (SNR) of ultrasonic signals, which degrades the UT performance. In this study, various deep learning (DL) techniques that can effectively extract the features of defects, even from signals with a low SNR, were applied to UT, and their performance in terms of the porosity evaluation of additively manufactured parts with rough surfaces was investigated. Experimentally, the effects of the processing conditions of additive manufacturing on the resulting porosity were first analyzed using both optical and scanning acoustic microscopy. Second, convolutional neural network (CNN), deep neural network, and multi-layer perceptron models were trained using time-domain ultrasonic signals obtained from additively manufactured specimens with various levels of porosity and surface roughness. The experimental results showed that all the models could evaluate porosity accurately, even that of the as-deposited specimens. In particular, the CNN delivered the best performance at 94.5%. However, conventional UT could not be applied because of the low SNR. The generalization performance when using newly manufactured as-deposited specimens was high at 90%.

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