{"id":3094,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3094"},"modified":"2022-07-26T13:03:53","modified_gmt":"2022-07-26T11:03:53","slug":"hybrid-ml-for-parameter-prediction-in-production","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/composite-materials\/hybrid-ml-for-parameter-prediction-in-production\/","title":{"rendered":"Hybrid-ML for parameter prediction in production"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Jonas Dorissen, Henrik Heymann, Robert H. Schmitt (Germany)<\/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\">In the past, research in the production domain was driven by mathematical and physical description of production technologies. In the last years, data-driven approaches like Machine Learning (ML) and Artificial Intelligence (AI) gave the research a new direction. Often, already exiting knowledge is neglected when using data-driven approaches resulting in models which do not represent the best possible results. By combining these two approaches all available knowledge is used generating the best possible model. This combination is called Hybrid Modelling. The article introduces hybrid-ML as part of Hybrid Modelling and demonstrates the benefits and challenges using hybrid-ML for the prediction of process parameters in the production domain.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Hybrid-ML; Hybrid Modelling; Machine learning; Artificial intelligence<\/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=\"1080\" style=\"aspect-ratio: 1920 \/ 1080;\" width=\"1920\" controls src=\"https:\/\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Henrik_Heymann_1.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=\"110\" class=\"wp-image-3923\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Henrik-Heymann.jpg?resize=150%2C110&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Henrik-Heymann.jpg?w=432&amp;ssl=1 432w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Henrik-Heymann.jpg?resize=300%2C221&amp;ssl=1 300w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Henrik-Heymann.jpg?resize=150%2C110&amp;ssl=1 150w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Henrik Heymann<br><br>Fraunhofer Institute for Production Technology IPT, Germany<br><br>henrik.heymann@ipt.fraunhofer.de<\/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 Jonas Dorissen, Henrik Heymann, Robert H. Schmitt (Germany) Abstract In the past, research in the production domain was driven by mathematical and physical description of production technologies. In the last years, data-driven approaches like Machine Learning (ML) and Artificial Intelligence (AI) gave the research a new direction. Often, already&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/composite-materials\/hybrid-ml-for-parameter-prediction-in-production\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":9,"featured_media":0,"parent":3652,"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-3094","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3094","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\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=3094"}],"version-history":[{"count":2,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3094\/revisions"}],"predecessor-version":[{"id":3925,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3094\/revisions\/3925"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3652"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3094"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}