Title: Supervised and unsupervised learning for characterising the industrial material defects

Authors: P. Radha; N. Selvakumar; J. Raja Sekar; J.V. Johnsonselva

Addresses: Department of Computer Applications, Mepco Schlenk Engineering College, Sivakasi, India ' Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, India ' Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India ' Department of Computer Applications, Mepco Schlenk Engineering College, Sivakasi, India

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.

Keywords: ultrasonic testing; internet of things; IoT; machine learning; density-based clustering; deep learning; deep neural network; DNN.

DOI: 10.1504/IJBIDM.2022.124852

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.2, pp.233 - 246

Received: 06 Jan 2021
Accepted: 24 Feb 2021
Published online: 11 Aug 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article