Estimation of an elasto-geometric model exploiting a loaded circular test on a machine tool
2022 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 123, no 7-8, p. 2331-2349Article in journal (Refereed) Published
Abstract [en]
A novel elasto-geometric model is introduced that simultaneously estimates joint compliances and geometric error parameters by employing the loaded double ball bar apparatus. The model parameters are estimated from tests at different force levels by distinguishing between errors that change with the applied force (compliance effect) from those that do not (geometric effects). At lower forces, the geometric errors are dominant while at higher forces compliance errors dominate. Using all data to build a single global geometry and compliance set of parameters (global constant compliance model), the radial volumetric variations due to geometric errors and compliance are estimated at 0.019 mm and 0.046 mm, respectively, making compliance dominant by more than three times. The impact of dominant and non-dominant equivalent global compliance CXXX, CYYY, CXYX, CCXY, CCYY, and CCCY on the loaded circular test readings at the highest force level of 742 N are predicted to be around 0.045, 0.034, 0.00058, 0.0022, 0.0014, and 0.0045 mm peak-to-peak, respectively. The impact of loaded geometric parameters EXX1, EYY1, EYX2, EXY2, EC(0Y)X, EXt0, and EYt0 on the loaded circular test readings is predicted to be around 0.019, 0.014, 0.0074, 0.012, 0.00017, 0.0076, and 0.0012 mm peak-to-peak, respectively. The dominant global compliances are CXXX and CYYY at 0.0619 and 0.0461 μm / N , respectively.
Place, publisher, year, edition, pages
Springer Nature , 2022. Vol. 123, no 7-8, p. 2331-2349
Keywords [en]
Elasto-geometric model, Geometric error, Joint compliance, Machine tool, Numerical simulation, Errors, Geometry, Parameter estimation, Circular tests, Double ball bar, Error parameters, Force level, Geometric errors, Geometric models, Joint compliances, Modeling parameters, Peak-to-peak, Machine tools
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:kth:diva-328885DOI: 10.1007/s00170-022-10324-xISI: 000875562200003Scopus ID: 2-s2.0-85140990331OAI: oai:DiVA.org:kth-328885DiVA, id: diva2:1766701
Note
QC 20230613
2023-06-132023-06-132023-06-13Bibliographically approved