{"id":3420,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3420"},"modified":"2022-07-26T13:01:59","modified_gmt":"2022-07-26T11:01:59","slug":"a-fuzzy-based-decision-making-approach-for-metal-additive-manufacturing-process-optimization","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/additive-manufacturing-3\/a-fuzzy-based-decision-making-approach-for-metal-additive-manufacturing-process-optimization\/","title":{"rendered":"A fuzzy-based decision-making approach for metal additive manufacturing process optimization"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Gennaro Salvatore Ponticelli, Simone Venettacci, Flaviana Tagliaferri, Oliviero Giannini, Stefano Guarino (Italy)<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Abstract<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Metal additive manufacturing processes are generally governed by a complex interaction of many process parameters, from those connected with the energy source, to those concerning the processed material. Therefore, making decisions and deducing control actions require considering different sources of uncertainty, both aleatoric, e.g. due to the process variability, and epistemic, e.g. due to the inability to describe accurately the physics of the process. The proposed fuzzy-based decision-making approach overcomes this difficulty as it incorporates imperfect information into a decision model for metal additive manufacturing optimization by taking into account both the random and the systematic errors.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Fuzzy logic; Genetic algorithm; Decision-making; Process optimization; Metal additive manufacturing; Laser powder bed fusion<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Video presentation<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"720\" style=\"aspect-ratio: 1280 \/ 720;\" width=\"1280\" controls src=\"https:\/\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Simone_Venettacci.mp4\"><\/video><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Presenting author<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-background\" style=\"background-color:#f3f4f5\"><tbody><tr><td><\/td><td><\/td><td><\/td><\/tr><tr><td><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"179\" class=\"wp-image-3254\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Simone_Venettacci_Photo.jpg?resize=150%2C179&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Simone_Venettacci_Photo.jpg?w=383&amp;ssl=1 383w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Simone_Venettacci_Photo.jpg?resize=251%2C300&amp;ssl=1 251w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Simone_Venettacci_Photo.jpg?resize=126%2C150&amp;ssl=1 126w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Simone Venettacci<br><br>University of Rome &#8220;Nicola Cusano&#8221;, Italy<br><br>simone.venettacci@unicusano.it<\/td><\/tr><tr><td><\/td><td><\/td><td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>by Gennaro Salvatore Ponticelli, Simone Venettacci, Flaviana Tagliaferri, Oliviero Giannini, Stefano Guarino (Italy) Abstract Metal additive manufacturing processes are generally governed by a complex interaction of many process parameters, from those connected with the energy source, to those concerning the processed material. Therefore, making decisions and deducing control actions require&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/additive-manufacturing-3\/a-fuzzy-based-decision-making-approach-for-metal-additive-manufacturing-process-optimization\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":4,"featured_media":0,"parent":3648,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-3420","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3420","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=3420"}],"version-history":[{"count":1,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3420\/revisions"}],"predecessor-version":[{"id":3424,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3420\/revisions\/3424"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3648"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3420"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}