| contributor author | Butler, Quade;Ziada, Youssef;Stephenson, David;Andrew Gadsden, S. | |
| date accessioned | 2022-12-27T23:16:11Z | |
| date available | 2022-12-27T23:16:11Z | |
| date copyright | 6/22/2022 12:00:00 AM | |
| date issued | 2022 | |
| identifier issn | 1087-1357 | |
| identifier other | manu_144_10_100802.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4288255 | |
| description abstract | The 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. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Condition Monitoring of Machine Tool Feed Drives: A Review | |
| type | Journal Paper | |
| journal volume | 144 | |
| journal issue | 10 | |
| journal title | Journal of Manufacturing Science and Engineering | |
| identifier doi | 10.1115/1.4054516 | |
| journal fristpage | 100802 | |
| journal lastpage | 100802_28 | |
| page | 28 | |
| tree | Journal of Manufacturing Science and Engineering:;2022:;volume( 144 ):;issue: 010 | |
| contenttype | Fulltext | |