{"id":3140,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3140"},"modified":"2022-07-26T13:05:43","modified_gmt":"2022-07-26T11:05:43","slug":"cyber-physical-optimization-of-production-processes-using-two-ais-a-robot-guided-mag-welding-use-case","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/forming-welding-2\/cyber-physical-optimization-of-production-processes-using-two-ais-a-robot-guided-mag-welding-use-case\/","title":{"rendered":"Cyber-Physical Optimization of Production Processes Using Two AIs: A Robot-Guided MAG Welding Use-Case"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Peter Burggraef, Fabian Steinberg, Philipp Nettesheim, Marian Vedder, Gerald Kolter (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\">To increase welding quality, in this work a cyber-physical system is presented using two cooperating AIs. The first AI uses supervised learning to initialize welding paths and parameters based on 3D scans of all parts that are inserted in the welding fixture. The second AI uses reinforcement learning to fine-tune the welding parameters based on the welding gap detected by a camera mounted in the top of the welding nozzle. The goals of this setup are to get insights into the interaction between two independent regulations using different machine learning algorithms and to increase the quality of the welded parts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Artificial Intelligence; Machine Learning; Deep Learning; Industry 4.0; Cyber Physical System; Welding<\/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\/Gerald_Kolter.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=\"140\" class=\"wp-image-3935\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Gerald-Kolter.jpg?resize=150%2C140&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Gerald-Kolter.jpg?w=514&amp;ssl=1 514w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Gerald-Kolter.jpg?resize=300%2C281&amp;ssl=1 300w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Gerald-Kolter.jpg?resize=150%2C140&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>Gerald Kolter<br><br>University of Siegen, Germany<br><br>gerald.kolter@uni-siegen.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 Peter Burggraef, Fabian Steinberg, Philipp Nettesheim, Marian Vedder, Gerald Kolter (Germany) Abstract To increase welding quality, in this work a cyber-physical system is presented using two cooperating AIs. The first AI uses supervised learning to initialize welding paths and parameters based on 3D scans of all parts that are&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/forming-welding-2\/cyber-physical-optimization-of-production-processes-using-two-ais-a-robot-guided-mag-welding-use-case\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":9,"featured_media":0,"parent":3656,"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-3140","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3140","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=3140"}],"version-history":[{"count":2,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3140\/revisions"}],"predecessor-version":[{"id":3936,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3140\/revisions\/3936"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3656"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3140"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}