A research team from the University at Buffalo (UB) is leveraging artificial intelligence (AI), simulation and big data tools to modernize manufacturing systems and improve the quality, production, and efficiency of industries like 3D printing.
Funded by a $2.3 million National Science Foundation (NSF) grant, the STREAM framework aims to create a public online repository where researchers and industry professionals can share information regarding data, models, simulators, controllers, and analytics, alongside a number of other research tasks.
“A commercial product is the end result of a long chain of interwoven steps that may span geography, industries and different manufacturing processes,” said Hongyue Sun, Assistant Professor of industrial systems engineering at UB. “Each step may be optimized, but that doesn’t always mean it’s for the greater good of the overall production process. What we’re doing is creating an analytical framework that connects and coordinates all 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.”
UB’s 3D printing research
UB researchers have previously used AI, machine learning (ML) and other technologies to make improvements to additive manufacturing processes.
Back in 2018, a UB team developed PrinTracker, a 3D printer identification system capable of tracking printed objects back to the machine that created them. As a result, the system could help law enforcers crack down on the potential unethical and criminal use of 3D printers.
More recently, in 2020, a UB team worked with MIT to develop a new ferroelectric metamaterial for photopolymerization 3D printing, reportedly marking a “major leap” toward making synthetic materials more affordable and suitable for various applications like electronics.
Modernizing manufacturing systems
The goal of the STREAM framework is to bring the complex web of steps and processes within the supply chain under the coordinated control of a sophisticated, connected computer system. The framework will make use of AI, simulation, and other ‘Industry 4.0’ technologies to streamline and connect the different elements of supply chains within industries such as 3D printing and semiconductors.
The supply chains for these sectors have numerous dependent steps, particularly the semiconductor industry.
“This includes tens of stages such as crystal growth, ingot slicing, wafer lapping and polishing, lithography, etching, chemical mechanical planarization,” Sun said. “These stages have strong dynamics and dependencies. The operations at downstream stages are affected by operations at upstream stages, quality-wise and productivity-wise.
“For instance, multiple lapping machines need to collaboratively process hundreds of wafers from ingot slicing; and the real-time process and production information of machines are interdependent and jointly determine the system’s performance.”
Throughout the project, the UB team will create software that enables efficient communication and computing within cyber-manufacturing systems. Additionally, the team will produce a modeling system to achieve an accurate and efficient process quality control.
The project will also see the creation of a simulation and production control system for the continuous improvement of quality, manufacturability, and productivity of future multistage and distributed manufacturing systems.
The ultimate goal of the project is to create an online repository for the sharing of information and experiences between researchers and industry professionals. The system will facilitate knowledge sharing regarding data, models, simulators, controllers, analytics and empirical studies, in a bid to foster the future development of highly intelligent and interactive manufacturing ecosystems that integrate product design, production, and logistics.
As part of the project, the UB researchers will create new outreach and workforce development activities for K-12, undergraduate and graduate students, as well as working with professionals in the manufacturing field.
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Featured image shows a 3D printer’s fingerprint. Image via Wenyao Xu, University at Buffalo.