3 Purdue Engineering faculty honored among 20 Most Influential Academics in Smart Manufacturing
SME Media’s Smart Manufacturing magazine, assisted by industry peers and manufacturing experts, selected academics who are “educating and shaping the next generation of engineers and smart manufacturing technologists across a diverse range of disciples.”
The Purdue Engineering honorees are:
John W. Sutherland
Professor and Fehsenfeld Family Head of Environmental and Ecological Engineering
Sutherland wants sensors to do more. In smart manufacturing, sensors can collect data on real-time key performance indicators, such as process throughput. But sensed data can also provide people making decisions with information related to environmentally relevant performance measures, such as energy consumption, resource use and waste creation, he said.
“This data can be processed through AI (artificial intelligence) algorithms to provide insights into how to reduce waste and increase energy and resource efficiency at the operation, shop-floor, and enterprise levels – all focused on moving manufacturing toward the goal of sustainability,” Sutherland explained.
Sutherland and his students have focused on collaborating with companies to create innovations related to sustainable manufacturing that avoid waste and better use material resources and energy. “As we move into the future, we want to use smart manufacturing technologies as a means to help our industry partners excel in terms of both traditional productivity and environmentally-oriented performance measures,” he said.
A member of SME longer than 30 years, Sutherland said his involvement in the North American Manufacturing Research Institution of SME has been a major part of his career.
Martin Byung-Guk Jun
Associate Professor of Mechanical Engineering
Jun’s research is helping to solve an engineering problem hindering Industry 4.0: What to do about legacy machine tools that still have years of life left in them but were not designed to provide the data needed to improve operations.
“I am currently involved with supporting smart manufacturing research at small and medium enterprises,” he said. “A number of machines at these enterprises have been connected to the network and IoT (Internet of Things) devices, and sensors have been utilized to collect machine and process data. The real-time data and data analytics results are provided to the appropriate people at each company in order to help with their decisions.”
Jun has also held workshops and demonstrations to educate operators, engineers and managers at small and medium-size factories on IoT and smart manufacturing. In addition, with a stethoscope-like sensor he invented, Jun automated the ability of longtime factory workers to tell, by listening to a machine tool in action, whether things are running smoothly. The sensor and AI determine a machine tool’s process status and detect any anomaly in the process.
Yung C. Shin
Donald A. and Nancy G. Roach Distinguished Professor of Advanced Manufacturing, Mechanical Engineering
Shin would like to see closer collaboration between industry and academia, and he has made significant strides in that direction. “Many scientific tools developed in academia need to see the daylight in industry,” he said. “In order to make this happen, I want to make various digital models more computationally efficient and robust so that they can be easily adopted in industry.”
He has done extensive research and development of in-process monitoring techniques and digital simulation models for laser-based manufacturing processes, including additive manufacturing, machining, micromachining, shock peening, and surface heat treatment. Shin played a pioneering role in the development and application of laser-assisted machining, which is now adopted around the world. He also established the Center for Laser-Based Manufacturing at Purdue, with participation from major manufacturers like Boeing, Rolls-Royce, General Electric and Lockheed Martin.
Additionally, Shin has worked on various AI-based strategies to optimize and control manufacturing processes. For example, he and four company partners developed the generalized intelligent grinding advisory system (GIGAS) with funding from the National Institute of Standards and Technology. GIGAS reduces the cost of grinding processes.
The article on the 20 academic influencers is in the June 2021 issue of Smart Manufacturing.