Literature DB >> 28603291

Diagnostics for geometric performance of machine tool linear axes.

Gregory W Vogl1, M Alkan Donmez1, Andreas Archenti2.   

Abstract

Machine tools degrade during operations, yet knowledge of degradation is elusive; accurately detecting degradation of linear axes is typically a manual and time-consuming process. Manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. A method was developed to use data from an inertial measurement unit (IMU) for identification of changes in the translational and angular errors due to axis degradation. A linear axis testbed, established for the purpose of verification and validation, revealed that the IMU-based method was capable of measuring geometric errors with acceptable test uncertainty ratios.

Entities:  

Keywords:  Diagnostics; Error; Machine tool

Year:  2016        PMID: 28603291      PMCID: PMC5464421          DOI: 10.1016/j.cirp.2016.04.117

Source DB:  PubMed          Journal:  CIRP Ann Manuf Technol        ISSN: 0007-8506            Impact factor:   3.916


  1 in total

1.  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

  1 in total
  2 in total

1.  Identification of machine tool squareness errors via inertial measurements.

Authors:  Károly Szipka; Andreas Archenti; Gregory W Vogl; M Alkan Donmez
Journal:  CIRP Ann Manuf Technol       Date:  2019       Impact factor: 3.916

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

Authors:  Gregory W Vogl; N Jordan Jameson; Andreas Archenti; Károly Szipka; M Alkan Donmez
Journal:  Int J Mach Tools Manuf       Date:  2019       Impact factor: 7.880

  2 in total

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