First course of its kind prepares students for big data in manufacturing
MechSE assistant professor and director of the Automation and Digital Manufacturing Lab Chenhui Shao has created a new course in mechanical engineering—Manufacturing Data and Quality Systems—one of the first of its kind, not just at Illinois, but in the country. ME 498’s structure introduces students to industry environments, and its content prepares students for the future of the manufacturing industry.
Manufacturing collects the largest dataset of any industry. That data, often high-dimensional or noisy, goes underutilized because of a lack of adequate tools to analyze it. As professionals entering the manufacturing industry, many graduates are poorly prepared for tackling the big data analytics that are becoming more and more prevalent in manufacturing.
This reality motivated Shao to create his course. A large component of his research at Illinois studies how to incorporate big data into manufacturing to allow for smart, automated decision-making. Seeing the importance that this subject has in the future of manufacturing, Shao designed his course to prepare his students to enter the industry.
All the work done in the course utilizes data taken from real-world manufacturing situations—including a Caterpillar plant in China; the nanomanufacturing (nanoMFG) node; MechSE colleagues’ research; and Shao’s own research. These datasets are used in lecture, homework, and projects. Shao continuously revisits each problem throughout the semester, but he utilizes different approaches each time. This gives the students a comprehensive view of the problem and an understanding of the strengths and weaknesses of each data processing strategy.
“I always give them many examples from my research. I always tell them, look I have this problem – what do you think?” Shao said. “I always engage them in the examples, the problems.”
The assignments mimic industry in many ways. In the workforce one doesn’t choose their colleagues, so Shao chooses the groups that students work in for both homework and projects. The bulk of each student’s grade comprises a final project that analyses a real-world dataset. The project includes a letter of intent, presentation in front of a small panel of judges, and a technical report graded by industry experts.
“I try to teach my students how to be ready—not only on a technical knowledge level, but also in professional experience,” Shao said. “How do you communicate? In industry you don’t get credit if you don’t present it well. That’s very important.”
The course teaches students about statistical quality control and machine learning methods. In a single semester students cannot become experts in every data processing philosophy, but Shao said they will be acquainted with enough strategies that when they encounter a problem in the workforce they will know where to start.