University Researchers Creating Intelligent System to Support Hybrid Manufacturing for Industrial 3D Printing Applications
Hybrid manufacturing combines both additive and subtractive manufacturing technology in a single machine system, like equipping a CNC machine with 3D printing capabilities, to make things like propellers and advanced nuclear fuels. Many say it’s the future of the manufacturing industry, and University of Windsor PhD student Shane Peelar seems to agree. The computer science researcher, and his supervisor Luis Rueda, recently received an Ontario Centres for Excellence (OCE) Talent Edge Program fellowship to create an intelligent system that supports hybrid manufacturing.
The system is for industry partner CAMufacturing Solutions Incorporated, which develops CAD/CAM software for companies that use 3D printing together with traditional manufacturing methods like casting or welding.
CAMufacturing Solutions president Bob Hedrick says about 3D printing, “This is so cool because it is a brand-new manufacturing process. People have been machining, casting and forging for hundreds of years, where artisans and craftsmen taught each other — but this technology is coming directly from academic research, from the high-end down.”
The computing R&D partnership between Peelar and Rueda and CAMufacturing Solutions, which will run for 16 months, will support industrial 3D printing applications, and tackle any challenges by writing advanced computer code to solve them.
As we know, 3D printing technology requires users to send digital data to the 3D printer itself, which will then print the part. But according to Peelar, it’s not always easy to get the engineering workstation computers so often found in machine shops to properly handle these large amounts of data.
Peelar explained, “The computational requirements of additive manufacturing for Computer-Aided Design/Computer-Aided Manufacturing or CAD/CAM software are a lot bigger than traditional methods — the datasets are very large and more suited to be performed on super-computers than PCs, and it is not uncommon for companies to want to run these programs on older hardware, computers that are four or five years old.
“We need to design scalable algorithms that can run and perform on older and newer hardware to allow additive manufacturing operations to be performed in a reasonable amount of time because companies don’t want this process to take eight hours; they want the software to run and produce parts in real time.”
CAMufacturing provides software to different manufacturing companies to build real end products, and not just replicas. These parts aren’t cheap, and could cause damage if they aren’t structurally sound. So getting the computers in the machine shop to work properly, starting with 3D modeling, is imperative for business.
“These are real machine tools, complex tools you can put right onto a robot to start working in the shop. We work with all sorts of industries — automotive, military, medical, mould shops, and aerospace companies. There is no end to the application of this,” Hedrick said.
This partnership is a great way to bridge the gap between advanced manufacturing and computational approaches that use machine learning and artificial intelligence to understand real life problems and properly guide industrial tools.
Dr. Rueda said, “This kind of technology, and the solutions being developed by CAMufacturing, are fascinating, unique and globally recognized. Likewise, this project is a great opportunity for collaboration between the university and the fast growing industrial sector in the region.”
According to Peelar, he is not only training the program to run in a timely fashion, but he is also teaching the machine model to learn from its previous experiences.
Hedrick is pleased with the “advanced and impressive” solutions that have resulted from working with the university researchers.
Peelar explained, “By training the machine model, using human operator knowledge, then like a chess program the model should start to recognize defects as well as things that are working well.
“Eventually the model will suggest areas for improvement and human operators can confirm if this is true, which will train the model, after several intervals, until eventually it works on its own to solve problems with reasonably high effectiveness.”
“We couldn’t do this type of work without academic researchers. We work with a lot of large companies that are manufacturing equipment, and the product being developed here is keeping pace with what these companies are doing right across the globe,” Hedrick said.
Discuss this and other 3D printing topics at 3DPrintBoard.com or share your thoughts in the Facebook comments below.[Source: University of Windsor]
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