Supervised and unsupervised learning for characterising the industrial material defects Online publication date: Thu, 11-Aug-2022
by P. Radha; N. Selvakumar; J. Raja Sekar; J.V. Johnsonselva
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 21, No. 2, 2022
Abstract: The ultrasonic-based NDT is used in industries to examine the internal defects without damaging the components since the materials used in the industrial standard components must be 100% perfect. The ultrasonic signals are difficult to interpret, and the domain expert has to concentrate at every sampling point to identify the defect. Hence, the existing ultrasonic-based NDT method is improved by applying IoT, machine learning, and deep learning techniques to process the ultrasonic data. This work integrates NDT and IoT to analyse the properties of materials using deep learning-based supervised model and filter outliers using unsupervised model like density-based clustering method. After analysing the different categories of defects, the notifications are sent to various stakeholders to either repair or replace the defective components through their mobile using advanced communication techniques to avoid expensive experimentation or maintenance.
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