{"id":3435,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3435"},"modified":"2022-07-26T13:03:53","modified_gmt":"2022-07-26T11:03:53","slug":"a-hybrid-approach-for-predictive-modeling-of-kpis-in-cnc-machining-operations","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cutting-technologies-2\/a-hybrid-approach-for-predictive-modeling-of-kpis-in-cnc-machining-operations\/","title":{"rendered":"A Hybrid Approach for Predictive Modeling of KPIs in CNC Machining Operations"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Vimala S.Vishnu, Kiran George Varghese, B. Gurumoorthy (India)<\/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 a CNC machining operation, key performance indicators (KPIs) of process, such as machining time, quality, and energy consumption, vary with cutting parameters. This paper explains a methodology for building physics-guided data-driven models for predicting these process KPIs in CNC machining operations from the planning, machining, and quality data. These physics-guided data-driven models are developed by combining data-driven and physics-based models of machining operations. Using hybrid physics-ML method, predictive modelling of energy consumption and surface roughness in CNC milling operation is also explained by conducting experiments. Finally, accuracies obtained by these models are compared with respective physics-based and data-driven models.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: CNC machining; Physics-guided data-driven modeling; Data analytics;<\/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\/Vishnu_VimalaSasidharan.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=\"195\" class=\"wp-image-3396\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Vishnu_VimalaSasidharan.jpg?resize=150%2C195&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Vishnu_VimalaSasidharan.jpg?w=423&amp;ssl=1 423w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Vishnu_VimalaSasidharan.jpg?resize=231%2C300&amp;ssl=1 231w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Vishnu_VimalaSasidharan.jpg?resize=116%2C150&amp;ssl=1 116w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Vimala Vishnu<br><br>Indian Institute of Science, India<br><br>bgm@iisc.ac.in<\/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 Vimala S.Vishnu, Kiran George Varghese, B. Gurumoorthy (India) Abstract In a CNC machining operation, key performance indicators (KPIs) of process, such as machining time, quality, and energy consumption, vary with cutting parameters. This paper explains a methodology for building physics-guided data-driven models for predicting these process KPIs in CNC&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/cutting-technologies-2\/a-hybrid-approach-for-predictive-modeling-of-kpis-in-cnc-machining-operations\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":4,"featured_media":0,"parent":3608,"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-3435","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3435","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=3435"}],"version-history":[{"count":1,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3435\/revisions"}],"predecessor-version":[{"id":3437,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3435\/revisions\/3437"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3608"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3435"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}