Research On Thermal Error Measurement And Modeling Of TK6920 Heavy Type Milling Boring Machine Tool

Heavy Type NC machine tools have got great importance today, because of the increasing demand for large parts in domestic heavy equipment manufacturing. Owing to its large structure and ranges of movement, shop floor temperature influences can deteriorate the whole volumetric performance; and their larger inertia and higher driving power that will generate much more heat, which transfer to frames caused thermal deformations. As a result of superposition of those deformations, it caused spindle thermal error and machining precision will be reduced.This thesis took the TK6920 heavy type milling boring machine tool as the research object, and focused on ―the research on thermal error modeling and measurement of TK6920 heavy type milling boring machine tool‖. Based on FEA, the whole machine’s temperature and deformation was simulated. With using Virtual Instrumentation Technology, thermal error and temperature measurement tools was designed and used in the thermal characteristics experiments of environment and different work conditions. According to analyze of the experiments, the multiple linear regressions thermal error models with robustly were established. The main contents as fellow:1. Based on the analyze of heavy milling boring machine heat source and corresponded boundary conditions, heat fluxes and heat convections boundary coefficient were calculated though theoretical models and empirical formulas; then temperature and deformation finite element model was established. The results of temperature and deformation of this machine tool was got from the model that was calculated by FEA.2. According to the characteristics of TK6920 machine temperature rise and thermal deformation, measurement tools and special fixtures were designed, and temperature and thermal deformation sensors were arranged reasonably for the thermal characteristics of the machine tool.3. Experiments concerned environment’s temperature in the vertical direction of the machine tool and different working conditions were undertaken. The effect of ambient temperature on the thermal deformation of the spindle was analyzed; also the different working states spindle thermal deformation rules were analyzed found that spindle thermal deformation changes were consistent with the spindle speed changes.4. Based on above analysis and thermal key points related select principles, the optimal key points were picked. In the light of linear regression and neural modeling way, the models were set up. By contrast, neural network thermal error models are not as good as linear regression models in forecast performance, under related working conditions. The number of linear regression models variables was optimized. The optimized models were confirmed by experiment and results showed the performance of the latter models are close to before designed models, but less key thermal points.