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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/31139
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dc.contributor.authorButler Q-
dc.contributor.authorZiada Y-
dc.contributor.authorStephenson D-
dc.contributor.authorAndrew Gadsden S-
dc.date.accessioned2025-02-27T14:48:11Z-
dc.date.available2025-02-27T14:48:11Z-
dc.date.issued2022-10-01-
dc.identifier.issn1087-1357-
dc.identifier.issn1528-8935-
dc.identifier.urihttp://hdl.handle.net/11375/31139-
dc.description.abstractThe innovations propelling the manufacturing industry towards Industry 4.0 have begun to maneuver into machine tools. Machine tool maintenance primarily concerns the feed drives used for workpiece and tool positioning. Condition monitoring of feed drives is the intermediate step between smart data acquisition and evaluating machine health through diagnostics and prognostics. This review outlines the techniques and methods that recent research presents for feed drive condition monitoring, diagnostics and prognostics. The methods are distinguished between being sensorless and sensor-based, as well as between signal-, model-, and machine learning-based techniques. Close attention is given to the components of feed drives (ball screws, linear guideways, and rotary axes) and the most notable parameters used for monitoring. Commercial and industry solutions to Industry 4.0 condition monitoring are described and detailed. The review is concluded with a brief summary and the observed research gaps.-
dc.publisherASME International-
dc.subject4014 Manufacturing Engineering-
dc.subject40 Engineering-
dc.subjectMachine Learning and Artificial Intelligence-
dc.subject9 Industry, Innovation and Infrastructure-
dc.titleCondition Monitoring of Machine Tool Feed Drives: A Review-
dc.typeArticle-
dc.date.updated2025-02-27T14:48:11Z-
dc.contributor.departmentMechanical Engineering-
dc.identifier.doihttps://doi.org/10.1115/1.4054516-
Appears in Collections:Mechanical Engineering Publications

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