AI checks spindle health
As with humans, age can take its toll on machine spindles. Over time, for example, a spindle can be damaged by a crash, drift out of balance or suffer adverse effects from lubrication starvation. Consequences eventually can include costly machine downtime and production of out-of-tolerance parts.
As with humans, age can take its toll on machine spindles. Over time, for example, a spindle can be damaged by a crash, drift out of balance or suffer adverse effects from lubrication starvation. Consequences eventually can include costly machine downtime and production of out-of-tolerance parts.
So Mazak Corp., Florence, Kentucky, is employing artificial intelligence to check the condition of spindles in some of its machines. The company’s new Spindle Health Monitoring System includes two sensors that feed data to an acquisition unit connected to an industrial computer for processing.
“The main things we’re using are vibration and current sensors to establish a baseline for the spindle,” said Joe Sanders, sales engineering assistant manager.
The system uses edge computing and data analytics based on a proprietary AI algorithm to model a spindle’s “signature.”
“Each spindle has its own signature — like a signal fingerprint — and no two are alike,” Sanders said.
The Spindle Health Monitoring System displays the percentage of remaining spindle life related to three factors. The lowest of these values is used as the remaining spindle life estimate as shown on the upper left of the screen. Image courtesy of Mazak
The Integrex i-300ST (below) is one of the horizontal machining centers equipped with Mazak’s Spindle Health Monitoring System. Image courtesy of Mazak
An initial 60-second, fixed-cycle test run establishes the baseline value for comparison during subsequent tests. Machine operators can run these tests anytime and view spindle health metrics on screens.
“The system tells you how the spindle compares to the ‘golden model’ that came from the data recorded,” Sanders said.
Presented metrics include spindle unbalance, lubrication condition and bearing health. Each is displayed as a percentage of remaining spindle life based on the individual metric, as shown in the image on Page 13. The lowest of these percentages is used as the overall estimate of remaining spindle life.
Over time, a neural network self-organizing map adds to the profile of each spindle, learning to better assess its health using information gleaned from an increasingly large dataset.
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