{"id":316,"date":"2019-06-02T12:10:12","date_gmt":"2019-06-02T16:10:12","guid":{"rendered":"https:\/\/mae.ncsu.edu\/cxu\/?page_id=316"},"modified":"2021-05-09T09:04:34","modified_gmt":"2021-05-09T13:04:34","slug":"ai-for-manufacturing-processes","status":"publish","type":"page","link":"https:\/\/mae.ncsu.edu\/cxu\/ai-for-manufacturing-processes\/","title":{"rendered":"AI in Advanced Manufacturing"},"content":{"rendered":"<div class=\"avada-row\">\n<div id=\"content\">\n<div id=\"post-58\" class=\"post-58 page type-page status-publish hentry\">\n<div class=\"post-content\">\n<div class=\"fusion-three-fourth three_fourth fusion-layout-column fusion-column last spacing-yes\">\n<div class=\"fusion-column-wrapper\">\n<h4><span style=\"font-size: 24pt\"><a href=\"https:\/\/mae.ncsu.edu\/cxu\/research\/videos\/\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color: #ff0000\"><strong>(<\/strong><\/span><\/a><span style=\"text-decoration: underline\"><a href=\"https:\/\/mae.ncsu.edu\/cxu\/research\/videos\/\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color: #ff0000\"><strong>Video link #3<\/strong><\/span><\/a><\/span><a href=\"https:\/\/mae.ncsu.edu\/cxu\/research\/videos\/\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color: #ff0000\"><strong>)<\/strong><\/span><\/a><\/span><\/h4>\n<p style=\"text-align: left\">Manufacturing is entering a period of substantial innovation and change driven by the increased integration of sensors and the Internet-of-things (IoT), increased data availability, and advances in robotics and automaton. This leads to pervasive digitalization of the factory and challenges manufacturing enterprises to reconsider, reexamine, and reevaluate their present operations and future strategic directions in the new era known as Smart Manufacturing and Industry 4.0. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data.<\/p>\n<p style=\"text-align: left\">The project that we\u00a0work with General Dynamics is on adaptive online control for open-architectured manufacturing processes. General Dynamics is one of the world leaders in defense, with revenues around $30 billion per year. With the advanced multivariable control method, the overall manufacturing costs were reduced by 10%. In conducting this research, General Dynamics provides the group a CNC lathe free usage for experimental validation on the project. It is an Okuma two-turret lathe, weighs about 25,000 lb, with accuracy of one ten-thousandth of an inch. The electric spindle drive motor is at 60 HP for heavy load turning operations.<\/p>\n<table style=\"border-collapse: collapse;width: 100%\">\n<tbody>\n<tr>\n<td style=\"width: 100%\">\u00a0<img loading=\"lazy\" decoding=\"async\" class=\"wp-image-249 aligncenter\" src=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/CNC-optimization_clip_image002.jpg\" alt=\"\" width=\"599\" height=\"449\" \/><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-253 aligncenter\" src=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image1-1024x768-600x450.jpg\" alt=\"\" width=\"600\" height=\"450\" srcset=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image1-1024x768-600x450.jpg 600w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image1-1024x768-768x576.jpg 768w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image1-1024x768-992x744.jpg 992w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image1-1024x768.jpg 1024w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/p>\n<p style=\"text-align: center\">Figure 1. Okuma LC40-2ST two-turret CNC lathe (a) Overview; (b) Inside settings.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<table style=\"border-collapse: collapse;width: 100%;height: 52px\">\n<tbody>\n<tr style=\"height: 26px\">\n<td style=\"width: 100%;height: 26px\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-447\" src=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/AI-for-manufacturing-600x312.png\" alt=\"\" width=\"761\" height=\"396\" srcset=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/AI-for-manufacturing-600x312.png 600w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/AI-for-manufacturing-768x400.png 768w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/AI-for-manufacturing.png 890w\" sizes=\"auto, (max-width: 761px) 100vw, 761px\" \/><\/p>\n<p style=\"text-align: center\">Figure 2. Multivariable adaptive neural network control scheme.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<p>&nbsp;<\/p>\n<table style=\"border-collapse: collapse;width: 100%\">\n<tbody>\n<tr>\n<td style=\"width: 100%\">\n<p style=\"text-align: center\">Table 1. Input and output variables to the CNC turning system, including measurement devices.<\/p>\n<p>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-452\" src=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/GD-600x505.png\" alt=\"\" width=\"600\" height=\"505\" srcset=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/GD-600x505.png 600w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/GD-768x646.png 768w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/GD.png 840w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<table style=\"border-collapse: collapse;width: 100%\">\n<tbody>\n<tr>\n<td style=\"width: 100%\">\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-563\" src=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image2-1024x768-600x450.jpeg\" alt=\"\" width=\"631\" height=\"473\" srcset=\"https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image2-1024x768-600x450.jpeg 600w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image2-1024x768-768x576.jpeg 768w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image2-1024x768-992x744.jpeg 992w, https:\/\/mae.ncsu.edu\/cxu\/wp-content\/uploads\/sites\/20\/2019\/06\/image2-1024x768.jpeg 1024w\" sizes=\"auto, (max-width: 631px) 100vw, 631px\" \/><\/p>\n<p style=\"text-align: center\">Figure 3. Manufactured workpieces through simultaneous turning\/boring operations.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"<p>(Video link #3) Manufacturing is entering a period of substantial innovation and change driven by the increased integration of sensors and the Internet-of-things (IoT), increased data availability, and advances in robotics and automaton. This leads to pervasive digitalization of the factory and challenges manufacturing enterprises to reconsider, reexamine, and reevaluate their present operations and future&hellip;<\/p>\n","protected":false},"author":281,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"no-sidebar.php","meta":{"_acf_changed":false,"ncst_dynamicHeaderBlockName":"","ncst_dynamicHeaderData":"","ncst_content_audit_freq":"","ncst_content_audit_date":"","ncst_content_audit_display":false,"ncst_backToTopFlag":"","footnotes":""},"class_list":["post-316","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/pages\/316","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/users\/281"}],"replies":[{"embeddable":true,"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/comments?post=316"}],"version-history":[{"count":10,"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/pages\/316\/revisions"}],"predecessor-version":[{"id":887,"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/pages\/316\/revisions\/887"}],"wp:attachment":[{"href":"https:\/\/mae.ncsu.edu\/cxu\/wp-json\/wp\/v2\/media?parent=316"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}