Literature DB >> 32116408

Root-cause analysis of wear-induced error motion changes of machine tool linear axes.

Gregory W Vogl1, N Jordan Jameson2, Andreas Archenti3, Károly Szipka3, M Alkan Donmez1.   

Abstract

Manufacturers need online methods that give up-to-date information of system capabilities to know and predict the performance of their machine tools. Use of an inertial measurement unit (IMU) is attractive for on-machine condition monitoring, so methods based on spatial filters were developed to determine rail wear conditions of linear guideways of a carriage from its IMU-based error motion. Rail wear-induced changes in translational and angular error motions as small as 1.5 μm and 3.0 microradians, respectively, could be resolved. A corresponding two-part root-cause analysis procedure was developed to determine the rail locations of error motion degradation as well as the most probable physical location of damage that causes the detected error motion changes. Another analysis method determined the root cause of non-localized damage along each rail. These approaches support the development of smart machine tools that provide actionable intelligence to manufacturers for early warnings of system degradation.

Entities:  

Keywords:  Condition monitoring; Diagnostics; Error; Machine tool; Wear

Year:  2019        PMID: 32116408      PMCID: PMC7047640          DOI: 10.1016/j.ijmachtools.2019.05.004

Source DB:  PubMed          Journal:  Int J Mach Tools Manuf        ISSN: 0890-6955            Impact factor:   7.880


  3 in total

1.  Diagnostics for geometric performance of machine tool linear axes.

Authors:  Gregory W Vogl; M Alkan Donmez; Andreas Archenti
Journal:  CIRP Ann Manuf Technol       Date:  2016-06-06       Impact factor: 3.916

2.  A Sensor-Based Method for Diagnostics of Machine Tool Linear Axes.

Authors:  Gregory W Vogl; Brian A Weiss; M Alkan Donmez
Journal:  Proc Annu Conf Progn Health Manag Soc       Date:  2015

3.  A wavelet bicoherence-based quadratic nonlinearity feature for translational axis condition monitoring.

Authors:  Yong Li; Xiufeng Wang; Jing Lin; Shengyu Shi
Journal:  Sensors (Basel)       Date:  2014-01-27       Impact factor: 3.576

  3 in total

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