{"id":3469,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3469"},"modified":"2022-07-26T13:06:17","modified_gmt":"2022-07-26T11:06:17","slug":"auto-identification-of-dynamic-axis-models-in-machine-tools","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/machine-tools-special-machines\/auto-identification-of-dynamic-axis-models-in-machine-tools\/","title":{"rendered":"Auto-identification of dynamic axis models in machine tools"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Alexander Puchta, Valentin Riegel, David Barton, Juergen Fleischer (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 metal-cutting manufacturing, ever smaller lot sizes lead to frequent changes in machining processes. For this, monitoring solutions help with setup and process optimization to achieve high quality and productivity at lower costs. For example, cutting forces may be monitored indirectly based on available data, like motor currents. However, this requires exact models of the individual dynamic behavior of machine axes. The determination of such models is time-consuming and cost-intensive. This paper presents an approach for the automatic identification of dynamic axis models, thus enabling an efficient deployment of force monitoring to a wide range of existing machines.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Digital manufacturing system; Identifcation; Machine tool<\/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\/Alexander_Puchta.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=\"225\" class=\"wp-image-3337\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?resize=150%2C225&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?w=1706&amp;ssl=1 1706w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?resize=200%2C300&amp;ssl=1 200w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?resize=683%2C1024&amp;ssl=1 683w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?resize=768%2C1152&amp;ssl=1 768w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?resize=1024%2C1536&amp;ssl=1 1024w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?resize=1365%2C2048&amp;ssl=1 1365w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Alexander_Puchta-scaled.jpg?resize=100%2C150&amp;ssl=1 100w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Alexander Puchta<br><br>Karlsruhe Institute of Technology, Germany<br><br>alexander.puchta@kit.edu<\/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 Alexander Puchta, Valentin Riegel, David Barton, Juergen Fleischer (Germany) Abstract In metal-cutting manufacturing, ever smaller lot sizes lead to frequent changes in machining processes. For this, monitoring solutions help with setup and process optimization to achieve high quality and productivity at lower costs. For example, cutting forces may be&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/machine-tools-special-machines\/auto-identification-of-dynamic-axis-models-in-machine-tools\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":296,"featured_media":0,"parent":3579,"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-3469","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3469","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\/296"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=3469"}],"version-history":[{"count":1,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3469\/revisions"}],"predecessor-version":[{"id":3470,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3469\/revisions\/3470"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3579"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3469"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}