Can Purdue Database Help Researchers Apply Machine Learning to 3D-Printed Parts in Machines?
WEST LAFAYETTE, IN—A team of Purdue University mechanical engineers has created the first comprehensive open-source annotated database of 3D mechanical parts to help researchers apply machine learning to those parts in actual machines.
“We are in the deep learning era, using computers to search for things visually,” says Karthik Ramani, Purdue’s Donald W. Feddersen Distinguished Professor of Mechanical Engineering. “But, no one is focusing on the parts that go into machines: pipes, bearings, motors, washers, nuts and bolts, etc. Those are the things that are important to us as engineers and manufacturers. We want to be able to point a camera at a real-world part, and have the computer tell us everything about that part or design.”