{"id":3455,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3455"},"modified":"2022-07-26T13:05:07","modified_gmt":"2022-07-26T11:05:07","slug":"visualization-of-relevant-areas-of-milling-tools-for-the-classification-of-tool-wear-by-machine-learning-methods-2","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cutting-technologies-2\/visualization-of-relevant-areas-of-milling-tools-for-the-classification-of-tool-wear-by-machine-learning-methods-2\/","title":{"rendered":"Visualization of relevant areas of milling tools for the classification of tool wear by machine learning methods"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Bjoern Papenberg, Sebastian Hogreve, Kirsten Tracht (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 production, the service life of milling tools must be optimally utilized. Machine learning methods are suitable for classifying the wear of milling tools. In this paper, a convolutional neural network is presented that classifies the wear of milling tools mapped on image data with an accuracy of 94.41 %. In addition, the ability of the convolutional neural network to detect relevant areas within the image is analyzed using gradient-weighted class activation mapping. The convolutional neural network is able to detect relevant areas in the image data, even though these areas are not marked in the input data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Tool wear; Machine learning<\/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\/Bjorn_Papenberg.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=\"113\" class=\"wp-image-2840\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Bjorn_Papenberg_Photo.jpg?resize=150%2C113&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Bjorn_Papenberg_Photo.jpg?w=315&amp;ssl=1 315w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Bjorn_Papenberg_Photo.jpg?resize=300%2C226&amp;ssl=1 300w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Bjorn_Papenberg_Photo.jpg?resize=150%2C113&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>Bjoern Papenberg<br><br>University of Bremen, Germany<br><br>papenberg@bime.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 Bjoern Papenberg, Sebastian Hogreve, Kirsten Tracht (Germany) Abstract In metal-cutting production, the service life of milling tools must be optimally utilized. Machine learning methods are suitable for classifying the wear of milling tools. In this paper, a convolutional neural network is presented that classifies the wear of milling tools&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/cutting-technologies-2\/visualization-of-relevant-areas-of-milling-tools-for-the-classification-of-tool-wear-by-machine-learning-methods-2\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":296,"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-3455","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3455","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=3455"}],"version-history":[{"count":1,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3455\/revisions"}],"predecessor-version":[{"id":3456,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3455\/revisions\/3456"}],"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=3455"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}