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A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites

https://doi.org/10.3390/jmmp7040148
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
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inferredcites →https://doi.org/10.1007/s11661-018-4621-4openalex-snapshot
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inferredcites →https://doi.org/10.1007/s11665-009-9507-6openalex-snapshot
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inferredcites →https://doi.org/10.1007/s13369-018-3559-6openalex-snapshot
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inferredcites →https://doi.org/10.1007/s42979-021-00592-xopenalex-snapshot
Machine Learning: Algorithms, Real-World Applications and Research Directions
inferredcites →https://doi.org/10.1016/j.actamat.2008.06.036openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.actamat.2019.03.010openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.cie.2022.108611openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compscitech.2005.05.016openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compscitech.2010.06.013openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jallcom.2006.08.145openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jmps.2007.10.004openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jmrt.2020.08.099openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jmst.2021.03.044openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.matdes.2009.11.015openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.matlet.2011.06.057openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.msea.2005.08.027openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.msea.2008.04.083openalex-snapshot
Three-dimensional representations of hardness distributions after processing by high-pressure torsion
inferredcites →https://doi.org/10.1016/j.msea.2008.08.021openalex-snapshot
The significance of strain reversals during processing by high-pressure torsion
inferredcites →https://doi.org/10.1016/j.pmatsci.2008.03.002openalex-snapshot
Using high-pressure torsion for metal processing: Fundamentals and applications
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inferredcites →https://doi.org/10.1016/j.rser.2014.01.069openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.wear.2006.01.023openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0893-6080(03)00169-2openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0921-5093(00)00919-9openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0921-5093(99)00764-9openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1359-6454(02)00466-4openalex-snapshot
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inferredcites →https://doi.org/10.1021/acs.chemmater.0c01907openalex-snapshot
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
inferredcites →https://doi.org/10.1021/acs.chemrev.1c00022openalex-snapshot
Gaussian Process Regression for Materials and Molecules
inferredcites →https://doi.org/10.1038/s41524-019-0221-0openalex-snapshot
Recent advances and applications of machine learning in solid-state materials science
inferredcites →https://doi.org/10.1038/s41586-018-0337-2openalex-snapshot
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inferredcites →https://doi.org/10.1080/14686996.2018.1435131openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.matdes.2025.114273openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.procbio.2026.08.016openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-025-10033-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-026-37295-3openalex-snapshot
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inferred← citeshttps://doi.org/10.1088/2053-1591/ae3a47openalex-snapshot
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inferred← citeshttps://doi.org/10.1108/ec-05-2025-0441openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cvidl70130.2026.11637756openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/09544089251370603openalex-snapshot
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inferred← citeshttps://doi.org/10.1364/ao.531620openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0300504openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/compounds3030034openalex-snapshot
Computer Modeling and Machine Learning in Chemistry and Materials Science: From Properties and Reactions of Small Organic and Inorganic Molecules to the Smart Design of Polymers and Composites
inferred← citeshttps://doi.org/10.3390/ma17102235openalex-snapshot
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inferred← citeshttps://doi.org/10.3934/matersci.2025011openalex-snapshot
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