Additive Manufacturing, or 3D printing, has helped manufacturing take giant leaps in the past few years. But one of the deficiencies in additive manufacturing is consistency. Subtle deviations in the geometric specifications of identical parts, even subtle variations, are a serious issue for precision applications like the aerospace industry. 3D printing has done wonders to save time with rapid prototyping and allowing engineers to design more complex parts, but the end product more often than not requires additional machining to achieve a precision fit. Artificial intelligence to the rescue.
The technology that makes it possible to reproduce hyper-accurate parts is being developed by researchers at Purdue University and the University of Southern California. “We’re really taking a giant leap and working on the future of manufacturing,” said Arman Sabbaghi, an assistant professor of statistics in Purdue’s College of Science, who led the research team at Purdue with support from the National Science Foundation. “We have developed automated machine learning technology to help improve additive manufacturing. This kind of innovation is heading on the path to essentially allowing anyone to be a manufacturer.”
The new technology allows end-users to run the programming interface locally, giving them the ability to utilize machine learning to analyze the product data and make adjustments that result in parts that fit together with a much higher degree of precision. The end result is parts that fit together as designed without the need for additional subtractive machining. “This has applications for many industries, such as aerospace, where exact geometric dimensions are crucial to ensure reliability and safety,” Sabbaghi said. “This has been the first time where I’ve been able to see my statistical work really make a difference and it’s the most incredible feeling in the world.”