<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning</dc:title><dc:creator>Smoqi, Ziyad; Gaikwad, Aniruddha; Bevans, Benjamin; Kobir, Md Humaun; Craig, James; Abul-Haj, Alan; Peralta, Alonso; Rao, Prahalada</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher/><dc:date>2022-06-01</dc:date><dc:nsf_par_id>10320570</dc:nsf_par_id><dc:journal_name>Journal of Materials Processing Technology</dc:journal_name><dc:journal_volume>304</dc:journal_volume><dc:journal_issue>C</dc:journal_issue><dc:page_range_or_elocation/><dc:issn>0924-0136</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1016/j.jmatprotec.2022.117550</dc:doi><dcq:identifierAwardId>1752069; 2020246; 1920245; 2044710; 2309483</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>