[et_pb_section fb_built=”1″ _builder_version=”4.27.9″ _module_preset=”default” custom_padding=”||2px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/affiliate.wcu.edu\/cet-news\/wp-content\/uploads\/sites\/405\/2026\/09\/WCU-Faculty-Machine-Shop-2026-7-scaled.jpg” title_text=”WCU-Faculty-Machine-Shop-2026-7″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Walk into a Computer Cyber-Physical Systems Lab and you might see a robotic arm plucking a single red block off a moving conveyor belt while ignoring every other color around it. It may look like a party trick. It isn\u2019t. That small, precise motion is a preview of something much bigger: a future where Western North Carolina\u2019s factories can teach their own machines to see, decide, and improve\u2014without ever handing over their most sensitive data.<\/p>\n
This fall, that vision got a serious boost. WCU has earned its first-ever Research Opportunities Initiative (ROI) grant<\/strong> from the UNC System: $1.5 million over three years, at $500,000 per year, to bring practical artificial intelligence to the manufacturers who power our region\u2019s economy. And while the project is led by Dr. Abdallah Abdallah<\/a><\/strong>, its real strength is a faculty team that brings together expertise from five engineering disciplines. WCU serves as the lead institution, with Dr. Abdallah\u2014associate professor in the College of Engineering and a certified NVIDIA Deep Learning Institute instructor\u2014as principal investigator, and a North Carolina State University team led by Dr. Ismail Guvenc<\/strong> contributing nationally recognized expertise in wireless communications under a sub-award.<\/p>\n AI is everywhere in the manufacturing headlines. On the factory floor, the reality is messier. For small and midsized manufacturers in Western North Carolina who know AI could help them, there are questions. Where do we start? What happens to our proprietary data? Will any of this actually pay off?<\/p>\n Those questions aren\u2019t signs of hesitation; they\u2019re signs of good judgment. A single production line can generate millions of sensor readings a week, and that data can be a company\u2019s most guarded asset. Ask a manufacturer to upload their data to someone else\u2019s cloud to train a model, and the conversation ends fast. This project is built around a different answer.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” text_text_color=”#592C88″ header_text_color=”#592C88″ global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text admin_label=”Text” _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The technical heart of the project is a Federated Learning Framework<\/strong>\u2014and it flips the usual AI recipe on its head. Instead of pooling everyone\u2019s data in one place to train a model, federated learning sends the model<\/em> out to each factory, trains it locally on data that never leaves the building, and then merges only the resulting model updates\u2014not the raw data\u2014into a shared, smarter global model.<\/p>\n That privacy guarantee has to hold up against determined adversaries, which is where co-investigator Dr. Islam Elgarhy<\/strong> comes in. His research spans federated learning, privacy-preserving methods, and cybersecurity, and he co-leads the design of the framework\u2019s algorithms\u2014hardening them so that even the shared model updates can\u2019t be reverse-engineered to leak a company\u2019s secrets. For an industry where competitors may sit two exits apart on the same interstate, that assurance is the difference between \u201cno thanks\u201d and \u201clet\u2019s try it.\u201d Think of it as a hive mind for factories\u2014the machines get collectively smarter while every plant keeps its secrets sealed in its own vault.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_text admin_label=”Text” _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n To make that framework trustworthy before it ever touches a live line, the team is building high-fidelity digital twins<\/strong>\u2014virtual replicas of a partner\u2019s actual machines. A digital twin lets researchers stress-test AI models against a faithful simulation of real equipment, tune them safely, and only then deploy to the physical floor. It shortens the distance between a promising idea and a production-ready tool, and it dramatically lowers the risk of disrupting a running operation.<\/p>\n Getting a twin to behave like the real thing takes more than software\u2014it takes deep knowledge of the physics inside each machine, and this is where the breadth of the WCU team shows. Dr. Bora Karayaka<\/strong><\/a>, a professor and IEEE Senior Member specializing in control systems and energy modeling, guides how the twins capture real-time control and energy-aware optimization. Dr. Mona Torabi<\/strong><\/a>, whose expertise is in advanced materials mechanics and multi-physics characterization, links a machine\u2019s process settings to how the material itself actually behaves under stress. And Dr. Basel Alsayyed<\/strong><\/a>\u2014a licensed Professional Engineer and Certified Manufacturing Engineer with more than a decade in the U.S. automotive industry\u2014anchors the manufacturing-process modeling and leads the team\u2019s push to identify patentable innovations along the way.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Quality control is the obvious win\u2014catching a defective part before it ships. But the more transformative application may be predictive maintenance<\/strong>. Sensing subsystems continuously track signals like vibration, temperature, and pressure; AI models learn each machine\u2019s healthy \u201cfingerprint\u201d; and when the data starts drifting, the system flags a likely failure before<\/em> the machine breaks down. Instead of unplanned downtime that halts a line, a plant gets a scheduled, planned repair\u2014often the single largest cost savings AI can deliver in manufacturing.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/affiliate.wcu.edu\/cet-news\/wp-content\/uploads\/sites\/405\/2026\/07\/WCU-Robotics-2.jpg” alt=”Green Hulk robot picks up red block” title_text=”WCU Robotics-2″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n None of this appeared out of nowhere. It grew out of ENGR 493, the \u201cAI and Computer Vision for Smart Manufacturing\u201d course<\/a><\/strong>, where student teams built three robots they named HULK<\/strong>, MEDUSA<\/strong>, and OPTIMUS<\/strong>. MEDUSA trained two robots to cooperate in spotting and removing a target item from a conveyor\u2014an automated defect-removal line in miniature. HULK used a machine-learning classifier to read visual cues and reposition the right colored block. OPTIMUS, a camera-equipped mobile robot, learned to detect, track, and retrieve a moving ball. Funded by a $35,000 UNC System Undergraduate Research Program Award, that course proved the concept and lit the fuse.<\/p>\n The ROI grant takes those same ideas out of the classroom and into real factories\u2014with real datasets, real constraints, and real stakes.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n What makes this team unusual is how deliberately interdisciplinary it is. Rather than a cluster of specialists in one narrow area, it pairs AI and cybersecurity (Dr. Abdallah and Dr. Elgarhy) with control and energy systems (Dr. Karayaka), advanced materials and mechanics (Dr. Torabi), and hands-on manufacturing and industrial engineering (Dr. Alsayyed).\u00a0<\/p>\n That range matters beyond the research itself. Drs. Karayaka, Torabi, and Alsayyed jointly lead the project\u2019s curriculum development and training workshops, translating the work into courses and hands-on experiences for students. And the grant funds a full talent pipeline: two postdoctoral researchers<\/strong> building the AI models, federated learning implementations, and digital twins, alongside graduate students, undergraduate summer projects, and capstone teams\u2014each shaped by the real requirements of industry partners so students solve the problems manufacturers are actually facing.<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Text” _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Several regional companies have signed on as non-paid collaborators, each bringing a different slice of the manufacturing world. Southern Container Inc.<\/strong>\u2014which produces more than 35% of the plastic bottle forms for Coca-Cola in North America\u2014will share production datasets and deep injection-molding expertise. Rhino Federated Computing<\/strong> contributes a free license to its federated learning software, putting a state-of-the-art platform in students\u2019 hands. And Lander Tubular Products<\/strong> of Franklin, NC, offers datasets and a site to pilot federated learning nodes in a working environment.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/affiliate.wcu.edu\/cet-news\/wp-content\/uploads\/sites\/405\/2026\/07\/WCU-Robotics-27.jpg” alt=”Faculty and students smile during demonstrations of the robots in the Spring 20256 ENGR 493\u2019s AI + Robotics course” title_text=”WCU Robotics-27″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text admin_label=”Text” _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The work is already drawing national attention. The Society of Manufacturing Engineers (SME)<\/strong>, a supporter of the project since its inception, selected Dr. Abdallah to serve as a volunteer advisor for its new AI and Digital Manufacturing Technical Community. Its inaugural meeting will be held alongside FABTECH 2026<\/strong>, October 21\u201323, 2026, at the Las Vegas Convention Center\u2014putting WCU\u2019s work in the same room as the industry shaping AI\u2019s future in manufacturing.<\/p>\n [\/et_pb_text][et_pb_testimonial author=”Dr. Abdallah Abdallah” job_title=”Associate Professor and Principal Investigator” company_name=”WCU College of Engineering” admin_label=”Testimonial” _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n \u201cMy goal is for WCU to become the hub regional manufacturers call when they need help integrating AI into their processes but aren\u2019t sure where to begin. They want qualified, trusted partners to walk that path with them.\u201d<\/em><\/p>\n [\/et_pb_testimonial][et_pb_text admin_label=”Text” _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_divider color=”#C1A875″ _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.27.9″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n What started as students coaxing robots to sort colored blocks has become a university-wide research venture\u2014uniting undergraduates, graduate students, postdocs, and a faculty team spanning five disciplines, all working with industry toward one mission. If it works the way Dr. Abdallah and his colleagues envision, WCU won\u2019t just be studying the future of smart manufacturing. It\u2019ll be the first call Western North Carolina\u2019s manufacturers make when they\u2019re ready to build it. The future of manufacturing, it turns out, may be assembled right here in Cullowhee\u2014no flying cars required, though the team isn\u2019t ruling anything out.<\/p>\n RELATED LINKS<\/strong><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.9″ _module_preset=”default” custom_margin=”-97px|||||” global_colors_info=”{}”][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" WCU has earned its first-ever Research Opportunities Initiative (ROI) grant from the UNC System: $1.5 million over three years, at $500,000 per year, to bring practical artificial intelligence to the manufacturers who power our region\u2019s economy.<\/p>\n","protected":false},"author":3447,"featured_media":11111,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[86],"tags":[],"class_list":["post-11097","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-wcu-engineering"],"_links":{"self":[{"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/posts\/11097","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/users\/3447"}],"replies":[{"embeddable":true,"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/comments?post=11097"}],"version-history":[{"count":24,"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/posts\/11097\/revisions"}],"predecessor-version":[{"id":11145,"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/posts\/11097\/revisions\/11145"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/media\/11111"}],"wp:attachment":[{"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/media?parent=11097"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/categories?post=11097"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/affiliate.wcu.edu\/cet-news\/wp-json\/wp\/v2\/tags?post=11097"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Federated learning: train together, share nothing<\/strong><\/h4>\n
Building a machine\u2019s digital twin\u2014and the physics inside it<\/strong><\/h4>\n
Machines that flag their own problems<\/strong><\/h4>\n
It started with HULK, MEDUSA, and OPTIMUS<\/strong><\/h4>\n
Five disciplines, one team<\/strong><\/h4>\n
Real partners, real production floors<\/strong><\/h4>\n
Recognized beyond the region<\/strong><\/h4>\n
The bigger picture<\/strong><\/h4>\n
\n