POSTED ON 3/22/2019 – Spindle downtime costs money in more ways than one. The failure of a single machine can take down an entire cell or disable a multi-machine production process. Downtime also can lead to a pattern of missed commitments that harms supplier/customer relationships. But without the ability to analyze spindle behavior and predict approaching failures, shops can find themselves in the middle of large, deadline-sensitive jobs with out-of-commission equipment, waiting for replacement parts that could take weeks to arrive. Rather than compromise profitability, shops need a maintenance program that anticipates problems – and new digital monitoring technologies can help screen out these avoidable setbacks.
In the past, preventive maintenance strategies have relied on examining finished work for flaws, listening for odd noises while machines operate and performing manual checks with testing devices such as ballbars and drawbar force gages. These procedures only work if shop personnel schedule and perform them correctly, filing prompt reports of any problems.
Rather than patch together inspections, shops increasingly look to automated technology and artificial intelligence (AI) to monitor equipment automatically and report wear before it causes critical faults. For example, after an AI-based solution takes a baseline assessment of a spindle, the monitoring system can make continued performance assessments and report impairment before it becomes expensive to repair. Organized, automatic condition monitoring also makes it possible to plan proactive maintenance at a time other than during peak production.