UB-led team advances cyber-manufacturing systems with $2.3 million NSF grant – UB Now: News and Views for UB Faculty and Staff

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A UB-led research team is working to modernize manufacturing systems, placing the complex web of steps and processes involved in creating commercial products under the watchful eyes of computer systems that promise increased efficiency.

The work, which is funded by a $2.3 million grant from the National Science Foundation, aims to help a range of industries – including semiconductor manufacturing and 3D printing – by improving the quality, production and efficiency.

“A commercial product is the end result of a long chain of intertwined steps that can span geography, industries, and different manufacturing processes,” says Hongyue Sun, the grant’s principal investigator and assistant professor of industrial engineering and science. systems.

“Each step can be optimized, but that doesn’t always mean it’s for the greater good of the entire production process,” he adds. “What we’re doing is creating an analytical framework that connects and coordinates all of these processes. The end result will be a cyber-physical system that uses artificial intelligence and other tools to optimize and ultimately improve manufacturing systems.

The project aligns with UB’s strategic plans to be a global leader in advanced manufacturing, including efforts to advance Industry 4.0, which is a term used to describe a fourth industrial revolution that consists of highly intelligent and interactive manufacturing ecosystems that integrate design, production and logistics.

The work also supports UB’s efforts to advance the economy of the greater Buffalo area.

UB co-principal investigators include Wenyao Xu, associate professor of computer science and engineering, and Chi Zhou, associate professor of industrial and systems engineering. Both Sun and Zhou are affiliated with the UB Community of Excellence (Sustainable Manufacturing and Advanced Robotic Technologies (SMART).

Additional co-principal investigators include Rong Pan, associate professor of computer science and augmented intelligence, and Guilia Pedrielli, assistant professor of computer science and augmented intelligence, both from Arizona State University.

The framework, which the team calls STREAM, includes the use of artificial intelligence, simulation and other technologies. It will create a public online repository where researchers and industry professionals can share information and their experiences regarding data, models, simulators, controllers, analysis and empirical studies.

In addition, the project includes three interconnected research tasks:

  • Creating software that enables effective communication and computing in cyber-manufacturing systems.
  • Creation of a modeling system to obtain an accurate and efficient quality control of the processes.
  • Creation of a simulation and production control system for the continuous improvement of the quality, manufacturability and productivity of future multi-stage and distributed manufacturing systems.

In the semiconductor manufacturing process, for example, according to Sun, there are many dependent steps.

“It includes dozens of steps, such as crystal growth, ingot slicing, wafer lapping and polishing, lithography, etching, chemical mechanical planarization,” he says. “These stages have strong dynamics and dependencies. Operations at downstream stages are affected by operations at upstream stages, in terms of quality and productivity.

“For example, multiple lapping machines need to collaboratively process hundreds of wafers of ingot wafers, and the real-time process and production information of the machines are interdependent and jointly determine the performance of the system,” he adds. .

As part of the project, researchers will create new outreach and workforce development activities for K-12, undergraduate and graduate students. They will also work with professionals in the field of manufacturing.

According to Sun, these research results could be included in the university’s advanced manufacturing, architecture design, machine learning, simulation, and system control and optimization curriculum.

The grant is part of NSF’s Future Manufacturing Research Grant CyberManufacturing project. It is jointly funded by the agency’s Computing and Networking Systems Division of the Computing and Information Science and Engineering Branch, and the divisions of Civil, Mechanical, and Manufacturing Innovation, and the Engineering Branch’s Training and Engineering Centers.


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