2023
DOI: 10.3390/robotics12050129
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A Novel Error Sensitivity Analysis Method for a Parallel Spindle Head

Liping Wang,
Mengyu Li,
Guang Yu

Abstract: Geometric errors are the main factors affecting the output accuracy of the parallel spindle head, and it is necessary to perform a sensitivity analysis to extract the critical geometric errors. The traditional sensitivity analysis method analyzes the output position and orientation errors independently, defining multiple sensitivity indices and making it difficult to determine critical geometric errors. In this paper, we propose sensitivity indices that can comprehensively consider position and orientation err… Show more

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Cited by 3 publications
(1 citation statement)
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“…Li established the position error model and motion error model of the turn-milling combined NC machine tool, and identified the important sensitive error terms by evaluating the influence weights of each error term [ 2 ]. Wang constructed the TCP error model of the 3-DOF parallel spindle head using the geometric errors, and got six critical geometric errors [ 3 ]. Fang built the geometric error model of the 6-axis welding equipment with 36 geometric error components based on Lie theory, and analyzed the sensitivity of geometric errors by numerical simulation, but no experimental proof was obtained [ 4 ].…”
Section: Introductionmentioning
confidence: 99%
“…Li established the position error model and motion error model of the turn-milling combined NC machine tool, and identified the important sensitive error terms by evaluating the influence weights of each error term [ 2 ]. Wang constructed the TCP error model of the 3-DOF parallel spindle head using the geometric errors, and got six critical geometric errors [ 3 ]. Fang built the geometric error model of the 6-axis welding equipment with 36 geometric error components based on Lie theory, and analyzed the sensitivity of geometric errors by numerical simulation, but no experimental proof was obtained [ 4 ].…”
Section: Introductionmentioning
confidence: 99%