Condition Monitoring of Machine Tool Feed Drives: A Review

被引：19

作者：

Butler, Quade ^{[1
]}

Ziada, Youssef ^{[2
]}

Stephenson, David ^{[2
]}

Gadsden, S. Andrew ^{[1
]}

机构：

[1] McMaster Univ, Dept Mech Engn, Hamilton, ON L8S 4L8, Canada

[2] Ford Motor Co, Global Mfg Engn, Livonia, MI 48150 USA

来源：

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME | 2022年 / 144卷 / 10期

关键词：

machine tool dynamics; machining processes; sensing; monitoring and diagnostics; EMPIRICAL MODE DECOMPOSITION; CONDITION-BASED MAINTENANCE; CONVOLUTIONAL NEURAL-NETWORK; WIRELESS SENSOR SYSTEM; BALL-SCREW DRIVES; FAULT-DIAGNOSIS; SIGNAL ANALYSIS; PRELOAD LEVELS; MOTOR CURRENT; ROTARY AXIS;

D O I：

10.1115/1.4054516

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：28

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