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contributor authorButler, Quade;Ziada, Youssef;Stephenson, David;Andrew Gadsden, S.
date accessioned2022-12-27T23:16:11Z
date available2022-12-27T23:16:11Z
date copyright6/22/2022 12:00:00 AM
date issued2022
identifier issn1087-1357
identifier othermanu_144_10_100802.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288255
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.
publisherThe American Society of Mechanical Engineers (ASME)
titleCondition Monitoring of Machine Tool Feed Drives: A Review
typeJournal Paper
journal volume144
journal issue10
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4054516
journal fristpage100802
journal lastpage100802_28
page28
treeJournal of Manufacturing Science and Engineering:;2022:;volume( 144 ):;issue: 010
contenttypeFulltext


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