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		<title>Machine learning reveals hidden pitfalls in predicting residual stress from tiny datasets</title>
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		<category><![CDATA[cladding]]></category>
		<category><![CDATA[conditions]]></category>
		<category><![CDATA[laser]]></category>
		<category><![CDATA[learning]]></category>
		<category><![CDATA[machine]]></category>
		<category><![CDATA[prediction]]></category>
		<category><![CDATA[residual]]></category>
		<category><![CDATA[Robust]]></category>
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		<category><![CDATA[small-sample]]></category>
		<category><![CDATA[stress]]></category>
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					<description><![CDATA[A robustness-oriented framework shows that validation strategy, not model complexity, decides whether machine learning predictions of laser cladding residual stress can be trusted.]]></description>
		
		
		
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