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<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>An optimized machine-learning tool to predict heat treatment response of hot-work tool steels</dc:title><dc:creator>Yarasu,	Venu	(Avtor)
	</dc:creator><dc:creator>Podgornik,	Bojan	(Avtor)
	</dc:creator><dc:subject>hot-work tool steel</dc:subject><dc:subject>machine learning</dc:subject><dc:subject>hardness</dc:subject><dc:subject>fracture toughness</dc:subject><dc:subject>graphical user interface</dc:subject><dc:publisher>Elsevier</dc:publisher><dc:date>2025</dc:date><dc:date>2025-05-22 08:39:51</dc:date><dc:type>Neznano</dc:type><dc:identifier>22499</dc:identifier><dc:identifier>UDK: 620.17:669.14: 004.85</dc:identifier><dc:identifier>ISSN pri članku: 2590-1230</dc:identifier><dc:identifier>DOI: 10.1016/j.rineng.2025.105260</dc:identifier><dc:identifier>COBISS_ID: 235640579</dc:identifier><dc:source>Results in Engineering</dc:source><dc:language>sl</dc:language><dc:rights>© 2025 The Authors. Published by Elsevier B.V.</dc:rights></metadata>
