Thermal displacement prediction of variable preload motorized spindles based on speed reduction experiments and IABC-BP optimization models

• DOI: 10.1016/j.csite.2023.103941
• Publication Date: 2023-12-25T15:46:16Z
• Authors: Ye Dai; Baolei Yu; Xueshi Tao; Xin Wang; Sai He; Gang Wang
• Keywords: Preload; Machine tool; Root mean square

The thermal error caused during the high-speed operation of a motorized spindle has significant impact on machine tool precision. In this paper, we firstly built, constructed an experimental platform for variable voltage preloaded spindle, used natural deceleration method to ascertain spindle's displacement and temperature information when bearing preload is 1400 N, 1450 1550 N 1700 combined with thermoelasticity theoretical analysis, affected by heat generation motor was separated, new artificial bee colony algorithm-optimized BP neural network-based prediction model been presented. displacements spindles are separated network optimization novel based Improved Artificial Bee Colony Algorithm (IABC-BP) proposed transformer spindles. Combined data at preloads predicted, evaluation indexes obtained from simulation BP, ABC-BP, IABC-BP models compared. absolute (MAE) root-mean-square (RMSE), higher goodness-of-fit (R2), 20.37 % lower mean 26.83 compared traditional model. in paper provides more accurate compensation