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		<title>Machine Learning Predicts How Special Threaded Connections Survive Extreme Combined Loads</title>
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		<pubDate>Thu, 08 Oct 2026 15:27:56 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[contact pressure]]></category>
		<category><![CDATA[finite element analysis]]></category>
		<category><![CDATA[Latin hypercube sampling]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[machining parameters]]></category>
		<category><![CDATA[Mises stress]]></category>
		<category><![CDATA[oil and gas wells]]></category>
		<category><![CDATA[plastic strain]]></category>
		<category><![CDATA[sealing integrity]]></category>
		<category><![CDATA[SHAP analysis]]></category>
		<category><![CDATA[special threaded connections]]></category>
		<category><![CDATA[XGBoost]]></category>
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					<description><![CDATA[Researchers trained XGBoost machine learning models on automated finite element simulations to predict how special threaded connections respond to combined thermal, axial, and pressure loads, finding that thread taper is the most influential machining parameter.]]></description>
		
		
		
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