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Simplifying the supply chain Using The STREAM Framework

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Artificial intelligence (AI), simulation, and big data tools are being used by a research team at the University at Buffalo (UB) to update industrial systems and increase the quality, output, and efficiency of industries like 3D printing.

The STREAM framework, which is supported by a $2.3 million NSF grant, aims to build a public online repository where researchers and industry professionals can share data, models, simulators, controllers, and analytics, as well as a variety 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.”

The STREAM framework’s purpose is to bring the supply chain’s complex web of steps and processes under the coordinated management of a sophisticated, networked computer system. The framework will leverage artificial intelligence (AI), simulation, and other ‘Industry 4.0’ technologies to integrate and streamline supply chains in areas like 3D printing and semiconductors. The semiconductor industry, in particular, has multiple dependent processes in its supply chain.

A 3D printed ferroelectric lattice. (Credit: University of Buffalo)

“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.”


 

 



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