2023 ASEE Annual Conference & Exposition

Advanced Manufacturing International (AMI), a non-profit that provides professional development for small through medium manufacturers, partnered with LECS Energy Inc. to develop the Learning Integrated Manufacturing System (LIMS) as an Industry 4.0 applicable Industry Internet of Things (IIot) edge platform. The LIMS appliance provides an affordable easy connection to manufacturing processing systems and subsystems to facilitate the maximum use of Industry 4.0-driven technologies installed in a manufacturing facility. The LIMS accomplishes this assignment by incorporating its complete rules engine, over 50 Input/Output Interfaces, as well as analytics and statistics engines for the turn-key subsystem and system applications that can be implemented in a small or medium manufacturing environment that may not include in-house edge-computing experienced IT support personnel. The LIMS platform also provides tested control examples and system applications that promote quick adoption and adaptation for real-world industry implementations.

This targeted manufacturing market and cost also make the LIMS platform an excellent learning tool within two-year and four-year Engineering Technology programs focused on creating the future of work technical workforce to support the I 4.0 technology environment. An initiative instigated by the Florida Advanced Technological Education Center of Excellence (FLATE) with support from America Works, (an initiative of the National Institute of Standards and Technology (NIST), the NIST Manufacturing Extension Partnership (MEP) national network, FloridaMakes, (the NIST MEP Center in Florida), and the National Science Foundation (NSF), has been launched to demonstrate the LIMS’s learning tool’s Project Based Learning potential. A LIMS platform was distributed to each of the five state colleges with 2-year Engineering Technology programs in Florida. This paper reports on the results of the Informed Engineering Design approach the colleges utilized to build their unique “hands-on” applications. The paper’s focus is directed on the challenges and results from an urban and a rural college. It will also report on student learning outcomes related to (IIOT) as well as project opportunities to easily capture data from different independent sources and perform a variety of analyses on the data outputs relating to seemingly unconnected/unrelated data sources in a single process analysis.