{"id":2136,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=2136"},"modified":"2021-07-13T18:57:54","modified_gmt":"2021-07-13T16:57:54","slug":"optimization-of-dry-electrical-discharge-machining-of-stainless-steel-using-big-data-analytics","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/cutting-nontraditional-technologies\/optimization-of-dry-electrical-discharge-machining-of-stainless-steel-using-big-data-analytics\/","title":{"rendered":"Optimization of dry electrical discharge machining of stainless steel using big data analytics"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by <em>Saman Fattahi, AMM Sharif Ullah<\/em><\/em> <em>(Japan)<\/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\">Big data (datasets accessible through the Internet) coupled with machine learning arrangements constitute big data analytics, which is heavily resource-depended and creates inequality-only large organizations can sustain or utilize big data analytics, and medium and small organizations fall behind. This article presents a novel inequality-free big data analytics for process planning in medium and small enterprises. Big data, search mechanism, control and evaluation variables relevant datasets, uncertainty quantification using possibility distributions, and decision rules are the components of the proposed analytics. This article reports the characteristics of the analytics applied to optimizing dry electrical discharge machining conditions of stainless steel.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Optimization, Big Data Analytics, Electrical Discharge Machining, Uncertainty, Intelligent systems<\/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=\"http:\/\/cirpicme.org\/wp-content\/uploads\/2021\/07\/amm_sharif_ullah.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-subtle-light-gray-background-color has-background\"><tbody><tr><td><\/td><td><\/td><td><\/td><\/tr><tr><td><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"153\" class=\"wp-image-2087\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/amm_sharif_ullah.jpg?resize=150%2C153\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/amm_sharif_ullah.jpg?w=270&amp;ssl=1 270w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2021\/07\/amm_sharif_ullah.jpg?resize=147%2C150&amp;ssl=1 147w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>AMM Sharif Ullah<br><br>Kitami Institute of Technology, Japan<br><br>ullah@mail.kitami-it.ac.jp<\/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 Saman Fattahi, AMM Sharif Ullah (Japan) Abstract Big data (datasets accessible through the Internet) coupled with machine learning arrangements constitute big data analytics, which is heavily resource-depended and creates inequality-only large organizations can sustain or utilize big data analytics, and medium and small organizations fall behind. This article presents&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/cutting-nontraditional-technologies\/optimization-of-dry-electrical-discharge-machining-of-stainless-steel-using-big-data-analytics\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":9,"featured_media":0,"parent":2367,"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-2136","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2136","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=2136"}],"version-history":[{"count":2,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2136\/revisions"}],"predecessor-version":[{"id":2185,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2136\/revisions\/2185"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/2367"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=2136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}