A high-speed milling machine can run at 42,000 RPM as it fabricates high-quality machine components within tolerances of a few microns. Excessive wear in that environment can lead to a failure that ruins an expensive part, but it’s difficult to use physical means to detect wear on cutting surfaces: human operators can’t see it and detailed microscopic inspections are costly. The result is that many operators simply replace parts on a pre-determined schedule — every two months, perhaps — that ends up being overly conservative.
Enter software: in a paper delivered to the IEEE’s Industrial Electronics Society in Montreal last Thursday*, a group of researchers from Singapore propose a way to use low-cost sensors along with machine learning algorithms to accurately predict wear on machine parts — a technique that could cut costs for manufacturers by lengthening the lifespan of machine parts while avoiding failures.
The group’s demonstration is a promising illustration of the industrial Internet, which promises to bring more intelligence to machines by linking them to networks and integrating them with sophisticated software. Techniques from areas like machine learning, which can be computationally intensive, can thus be available in monitoring parts as small and common as cutting surfaces in milling machines.
“This is a simple optimization problem,” says Meng Joo Er, a professor at the Nanyang Technological University and an author of the paper. “But you’re talking about a very expensive piece of equipment working on a very expensive product. We have to be very careful.”
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