20.500.12556/DKUM-90645 Rapid assessment of steel machinability through spark analysis and data-mining techniques The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive and costly. This study presents a novel methodology to rapidly determine steel machinability using spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including various low-alloy and high-alloy steels, with most samples being calcium steels known for their superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15 values, which were measured using the standard ISO 3685 test. Our results demonstrate that the created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While some samples exhibited high MAPE values, the method overall provided accurate machinability predictions. Compared to the standard ISO test, which takes several hours to complete, our method is significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective and time-efficient alternative testing method, thereby supporting improved manufacturing processes. steel machinability spark testing data mining machine vision convolutional neural networks obdelovalnost jekla testiranje isker podatkovno rudarjenje strojni vid konvolucijske nevronske mreže true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/metals © 2024 by the authors Članek v reviji 2024-09-12 13:58:44 2024-09-12 13:58:44 2026-02-13 13:39:20 0000-00-00 00:00:00 2024 0 0 19 str. iss. 8, [article no.] 955 Vol. 14 Aug. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-07-03 2024-08-20 2024-08-22 621.7:004.8 207322883 10.3390/met14080955 2075-4701 metals-14-00955.pdf metals-14-00955.pdf 1 3137F172FF60892994F114D293233DA7 463cdd9528fa3c64a7e6ff77a69d5fe52f7c27a4e970aa9f0eedc0f8456007d0 c7eb337c-70fe-11ef-93e7-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=185031 https://www.mdpi.com/2075-4701/14/8/955 0 cfeaf602-70fe-11ef-93e7-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=185032 Fakulteta za strojništvo 0 0 0