2023 ASEE Annual Conference & Exposition

As IoT and AI continue to reshape industrial processes and product lifecycles, the need for retraining current workers and attracting future ones to the manufacturing industry has grown. Nationwide, The US manufacturing sector is expected to have 2.1M unfilled jobs by 2030, a shortage that will be led by gaps in filling and retaining skilled positions. The problem further intensifies because although the manufacturing workforce growth results in new jobs and higher wages, manufacturers face challenges in recruiting well-qualified workers and professionals. While reskilling and upskilling efforts will be needed for the current workforce, particularly in the floor plant, new jobs and occupations will emerge. These new jobs will require professionals and future managerial employees to have strong data science skills in order to effectively design and oversee future AI-enabled manufacturing systems. However, a critical gap exists between traditional analytic/numeric engineering education and computer science/AI development that can provide skills to effectively enact and manage the full data science cycle.

To take steps toward preparing engineering graduates to effectively work with data, starting from data collection through sensors to data analysis and insight enabled by dashboards, [Midwestern] University designed and implemented a graduate course in partnership with local industries. The course titled Industrial IoT Implementation for Smart Manufacturing provides an introduction to the industrial internet of things (IoT) implementation on real production machines for smart manufacturing. It is a practical lab/project course that allows engineering students to implement IoT sensors and devices on real production machines at local manufacturing companies, collect data, perform data analytics for the company’s benefit, and demonstrate the visualization of the analyzed data. Students worked with a local manufacturing company to support the implementation of sensors and devices to a production machine, collection of data, analytics, and visualization.

Kolb’s Experiential Learning model will be used as an explanatory approach to describe the course design and implementation. In the final version of this study, we will fully unpack the learning objectives of the course and how those were enacted by the students through the laboratory assignments and the final project implemented at a particular company. Specifically, we will describe how students enacted a learning process where new knowledge and skills resulted from the combination of grasping and transforming their experience through the acquisition of abstract concepts that were then practiced through laboratory experiences and then applied flexibly in a real-world industry situation. We will also describe some of the projects students’ implemented at local companies and the potential benefits or outcomes resulting from those implementations.