Year: 2024
Type: article
Abstract: In industrial manufacturing processes, detection of defects on the surfaces of metal plates supplied from iron and steel main industry manufacturers to be processed by machining and non-machining methods has an important place in estimating the values of the relevant plate such as safety and maintenance cost. With the developing technology and computer vision and deep learning applications finding a place in the industry, it has become possible to detect and classify metal plate surface defects more quickly and effectively with a lower error rate at an advanced technological level. Within the scope of this study, a deep learning model was created by using the TensorFlow library in the Python environment with using NEU Metal Surface Defects Dataset to detect metal plate surface defects. Then as an industrial application, a device prototype developed using Nvidia Jetson Nano and USB Camera, in order to test this model under real conditions.
DOI: 10.55546/jmm.1512549
Authors: Can Tuncer, Cemil Közkurt, Serhat Kılıçarslan
Institution: Bandırma Onyedi Eylül University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404891555