Efficient compensation of dimensional errors in robotic machining using imperfect point cloud part inspection data

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“Process-to-part” robotic machining using low-cost, off-the-shelf industrial robots is of interest for feature machining applications on large components where mounting on conventional machine tools is costly and presents a health and safety risk. For a range of well documented reasons, low-cost industrial robot-based machine tools are limited in terms of machining tolerance range. Work conducted by the authors in Barnfather et al. (2016a, 2016b, 2016c, 2017a, 2017b) investigates these capabilities as well as on-line dimensional error measurement and compensation using optical scanning methods, which have development potential for low cost dense error measurement. A key problem when optically scanning machined surfaces for error measurement is noise and localised data gaps due to reflectance. Robotic machining error compensation in this way can therefore be thought of as a two-part problem involving acquiring quality scan data and processing it optimally to compensate a machining trajectory. This paper contributes to the latter by presenting a dimensional deviation evaluation method for efficiently computing compensated robotic machining trajectories from aligned optically scanned point clouds without processing redundant data, as is typical in related works. Results validate this method, showing a ∼96% improvement on dimensional error measurement time in comparison to conventional methods, and conclusions are drawn on direction for further development. This method is a novel contribution to the current state of the art of point cloud processing for on-line robotic machining error measurement and compensation, allowing the best to be made of imperfect point cloud inspection scans.

Bibliographical metadata

Original languageEnglish
Pages (from-to)176-185
Number of pages10
JournalMeasurement: Journal of the International Measurement Confederation
Early online date14 Dec 2017
Publication statusPublished - 1 Mar 2018