{"id":206,"date":"2021-09-15T17:45:27","date_gmt":"2021-09-15T21:45:27","guid":{"rendered":"https:\/\/coefs.charlotte.edu\/ejoyee\/?page_id=206"},"modified":"2026-03-12T10:57:55","modified_gmt":"2026-03-12T14:57:55","slug":"intelligent-am-for-smart-fabrication","status":"publish","type":"page","link":"https:\/\/coefs.charlotte.edu\/ejoyee\/research\/intelligent-am-for-smart-fabrication\/","title":{"rendered":"Intelligent AM for Smart Fabrication"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"alignleft\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"200\" src=\"https:\/\/coefs.charlotte.edu\/ejoyee\/files\/2021\/08\/artificial-intelligence-applications-in-additive-manufacturing-3d-printing-scaled-1-300x200.jpeg\" alt=\"Intelligent AM for Smart Fabrication\" class=\"wp-image-113\" srcset=\"https:\/\/coefs.charlotte.edu\/ejoyee\/files\/2021\/08\/artificial-intelligence-applications-in-additive-manufacturing-3d-printing-scaled-1-300x200.jpeg 300w, https:\/\/coefs.charlotte.edu\/ejoyee\/files\/2021\/08\/artificial-intelligence-applications-in-additive-manufacturing-3d-printing-scaled-1-1024x683.jpeg 1024w, https:\/\/coefs.charlotte.edu\/ejoyee\/files\/2021\/08\/artificial-intelligence-applications-in-additive-manufacturing-3d-printing-scaled-1-768x512.jpeg 768w, https:\/\/coefs.charlotte.edu\/ejoyee\/files\/2021\/08\/artificial-intelligence-applications-in-additive-manufacturing-3d-printing-scaled-1-1536x1024.jpeg 1536w, https:\/\/coefs.charlotte.edu\/ejoyee\/files\/2021\/08\/artificial-intelligence-applications-in-additive-manufacturing-3d-printing-scaled-1-2048x1366.jpeg 2048w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p><\/p>\n\n\n\n<p>Human centered, experiment based process planning for multi-material AM is a major obstacle for widespread adoption of this technology, as it depends on a lot of empirical parameters. Application of optimization and machine learning (ML) techniques will be a potential solution to address the challenges associated with process parameter control and sustainable production planning for multi-material bio-inspired products.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Human centered, experiment based process planning for multi-material AM is a major obstacle for widespread adoption of this technology, as it depends on a lot of empirical parameters. Application of optimization and machine learning (ML) techniques will be a potential solution to address the challenges associated with process parameter control and sustainable production planning for [&hellip;]<\/p>\n","protected":false},"author":285,"featured_media":0,"parent":42,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-206","page","type-page","status-publish","czr-hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/pages\/206","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/users\/285"}],"replies":[{"embeddable":true,"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/comments?post=206"}],"version-history":[{"count":5,"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/pages\/206\/revisions"}],"predecessor-version":[{"id":651,"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/pages\/206\/revisions\/651"}],"up":[{"embeddable":true,"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/pages\/42"}],"wp:attachment":[{"href":"https:\/\/coefs.charlotte.edu\/ejoyee\/wp-json\/wp\/v2\/media?parent=206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}