Thermal modeling of workpiece temperature and distortion in MQL deep-hole drilling.

摘要

This research investigates the workpiece temperature and distortion in minimum quantity lubrication (MQL) deep-hole drilling. Due to the advantages of reducing machining cost and environmental impact, MQL has been widely implemented in production. However, deep-hole drilling is technically challenging for MQL application due to the poor capability for workpiece cooling. This research focuses on quantifying the heat flow to the workpiece as a function of depth and time and the associated thermal distortion in MQL deep-hole drilling.;Heat sources in deep-hole drilling are not only from the drill-workpiece interface on the hole bottom surface (HBS), but also through the hole wall surface (HWS) due to high temperature chips and drill margin friction. The resulting heat flux from these factors on HWS is solved by the inverse heat transfer method. This method has demonstrated to be able to estimate the temporal and spatial distributions of HWS heat flux in both dry and MQL conditions. The importance of HWS heat flux in deep-hole drilling of ductile iron has also been analyzed.;In MQL deep-hole drilling, air pressure and feed rate are important in HWS heat flux and workpiece temperature. These effects are investigated in a production MQL system and analyzed by the inverse heat transfer method. Chip accumulation under slow feed rate and low air pressure has shown to increase the heat flux on HWS significantly without being inspected by drilling torque and thrust force. Although the high air pressure can maintain a smooth chip evacuation under a slow drilling feed rate, it does not provide further improvement when the chips are evacuated properly under a high feed rate.;Based on the heat flux calculation, a 3-D finite element model is developed to predict the workpiece thermal distortion. This model excludes the advection removal of elements and the mechanical contact between drill and workpiece, thus it is more practical for implementation due to the reduced computation time. The accuracy of the model prediction is validated by measuring the distortion of an aluminum workpiece after drilling four deep-holes. Potential applications of this model include error compensation, optimization of clamping design and machining sequence.