Factory 4.0 Toolkit for Smart Manufacturing Training

The rapid pace of technology development in the field of smart manufacturing has left educational systems scrambling to keep pace and adapt learning outcomes, resulting in inadequate preparedness and readiness of workforce at all levels. Often, smart manufacturing training materials are either broad and conceptual or a specific technical deep dive with little context. We have developed an educational toolkit that leverages an inexpensive, bench scale extrusion platform to provide lab activities and feature-rich data to explore fundamental concepts of smart manufacturing in a production context for an audience of both undergraduate engineering students and current manufacturing workforce members. Through investigation of the mock production platform and associated data, concepts and applications of modern data-driven tools are explored in the topic areas of data collection and the industrial internet of things, data analytics and predictive modeling for production data, simulation and digital twinning, and process and manufacturing systems optimization. The activities culminate in the exploration of advanced feedback control algorithms and optimization of operating conditions, balancing throughput, quality, and power consumption, using digital twins. The combination of overview conceptual materials along with in-depth activities on an actual process allows us to tailor the scope of the specific training to the intended audience. Select modules of the Factory 4.0 toolkit were delivered in an undergraduate course and in a training workshop for manufacturing personnel. Pre- and post-attitude surveys, along with participant comments, were used to assess the training approach and content. We found that the proper technical scope is critical for a given audience and that all types of manufacturing personnel, from technicians and engineers to operations and management, benefit from foundational smart manufacturing concepts and examples. We also found that for technical materials, student audiences required more of the fundamental instrumentation and statistical analysis topics, while current technical practitioners desired specific deep dives into data analytics, digital twinning, and process optimization after introductory overviews. Both educational experiences exposed a need for preparedness in programming and statistical analysis software tools to take advantage of these smart manufacturing concepts.