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		<title>Advancing Weld Defect Detection with Hybrid Machine Learning</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sun, 11 Jan 2026 12:58:25 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Automated weld defect classification]]></category>
		<category><![CDATA[Gas metal arc welding]]></category>
		<category><![CDATA[Hybrid machine learning]]></category>
		<category><![CDATA[Industrial quality control]]></category>
		<category><![CDATA[Manufacturing Automation]]></category>
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					<description><![CDATA[In recent years, the rapid evolution of machine learning technologies has permeated various industries, showcasing a profound ability to revolutionize traditional methodologies. A vivid illustration of this transformative potential is the exploration conducted by researchers Senthamilarasi, C., Anbarasi, M.P., and Vinod, B., who have delved into the automation of weld defect classification through innovative hybrid [&#8230;]]]></description>
		
		
		
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