Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out how to print with these materials can be a complex, costly conundrum.
Often, an expert operator must use manual trial-and-error — possibly making thousands of prints — to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits.
MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.
As the Senior Manager of Industrial Tech and the Practice Lead of Manufacturing Information Systems at Feyen Zylstra, Mike Manzi applies his expertise from past careers to provide innovative solutions for FZ’s customers. With over 25 years of experience working in the Industrial Sector, Mike combines his expertise in the design, installation, control, operations, and maintenance of manufacturing process control systems with a passion for holistic, simplified approaches to the complex issues of today. Mike has a broad background in manufacturing that includes being a nuclear qualified US Navy machinist mate, an industrial electrician, an engineer at Rockwell Automation and a major GE Representative (Gray Matter Systems), and a global staff engineer for both PPG and Kennametal. Mike graduated Magna Cum-Laude from Cleveland State University with a bachelor’s degree in electrical engineering and controls and currently sits on Cleveland State’s Industrial Advisory Committee.
