In this new digital age, efficiency, quality and competition are all increasing rapidly as companies leverage the Industrial Internet of Things (IIoT). However, while industrial innovation moves at a faster and faster pace, educational institutions have lagged in the development of the curriculum and environment needed to support further development of the IIoT. To fully realize the potential of the IIoT in the manufacturing sector educational institutions must support the technological training and education rigor demanded to instill the skills and thought leadership to move the industry forward. The purpose of this research is to provide an IIoT core system in an educational factory environment. This system will assist in teaching basic principles of IIoT in the factory while simultaneously allowing for students to envision the manufacturing journey of any facility by implementing principles of IIoT. This will be accomplished by providing all the following capabilities together in a single data system: unified connectivity, role-based data display, real-time issue identification, data analytics, and augmented reality.

With the advent of the fourth industrial revolution, information technology and manufacturing systems are merging to form what is now known as Smart Manufacturing. However, with this newer technology being integrated with newer pieces of machining equipment, companies with legacy equipment occasionally are in a bind since these machines were not designed or built with the fundamental components of smart manufacturing systems: unified connectivity, real-time monitoring, and issue identification. The purpose of this research is to provide a solution for converting manual machining equipment into smart systems with these fundamental components of smart manufacturing. The pieces of equipment that were the subjects of this experimentation were an HJ-1100 Kingston lathe and four ACER Vertical Turret Milling machines. None of these machines had any of these capabilities at the inception of this project.These machines were successfully converted into smart systems with varying degrees of reliability between the lathe and the four mills in the case of real-time monitoring and issue identification. The setups and configurations to achieve these three smart components are described and provided.