Machine learning for making machines: Applying visual search to mechanical parts

A new database would help engineers and manufacturers to apply machine learning to mechanical parts. Computer vision researchers use machine learning to train computers in visually recognizing objects--but very few apply machine learning to mechanical parts such as gearboxes, bearings, brakes, clutches, motors, nuts, bolts and washers. A team of Purdue University mechanical engineers has created the first comprehensive open-source annotated database of more than 58,000 3-D mechanical parts, designed 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," said 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."