Predictive Modeling Of Residual Stress In Minimum Quantity Lubrication Machining

Residual stress is one of the critical characteristics for assessing the surface integrityof machined components as it poses a strong bearing on the service quality, functionality,and life of the machined components. Also, residual stress has a significant effect on thedimensional accuracy of the machined components. In addition, the machining residualstresses can not be duplicated or reproduced. As a result, it is vital for industrialmanufacture to accurately predict and control the machining residual stresses.In metal cutting process, the residual stresses of machined components can beaffected by many factors. It mainly includes cutting parameters, tool geometry, materialproperties, and lubrication conditions. Minimum quantity lubrication (MQL) machiningrefers to the use of a small amount of cutting fluid, which was sprayed to the cutting zonewith the compressed air. Compared with the conventional flood cooling machining, MQLmachining reduces the costs of cutting fluids and its ancillary equipment, and alsoalleviates the environment pollution. The quality of machined components produced byMQL is close or better than that produced by conventional flood cooling machining.Furthermore, the lubrication system of MQL is low-cost and easy-operate. Therefore,controlling of the machining residual stresses through MQL has a strong competitiveadvantage for industry application.This paper presents an analytical model that predicts the residual stresses in MQLmachining as functions of cutting parameters, tool geometry, material properties, as wellas MQL application parameters. The main research contents are as follows:(1) Modeling the effects of MQL on tribological attributes in mechanicalmachining First, the permeation mechanisms of cutting fluids under two different lubricationconditions are introduced. For flood cooling machining, there are three penetrationprocesses: liquid phase penetration, micro-droplet evaporation, and gaseous phase filling.For MQL machining, the air-oil mixture permeated into the friction interface bygas-liquid two-phase. Second, two prominent effects caused by MQL air-oil mixture inmachining are discussed. For the lubrication effect, the boundary lubrication model isadopted to estimate the friction coefficient in MQL machining. So that the change offriction force can be estimated. For the cooling effect, the heat transfer coefficient inMQL machining is predicted based on the forced convection cooling method, and theheat loss method is employed to predict the cutting temperature due to heat loss.(2) Force and temperature coupled prediction modelFirst, the cutting force and cutting temperature are coupled by the flow stress. Inmetal cutting process, the cutting temperature is generated due to the cutting force, and itis known to affect the flow stress according to the Johnson-Cook material constitutivemodel. The changes of flow stresses will lead to the variation of cutting force. The cuttingforce and cutting temperature reach the dynamic balance by the coupling method. Second,the cutting force and cutting temperature prediction model in MQL machining isestablished. The cutting force in dry machining is estimated as the initial cutting force topredict the friction coefficient through the lubrication model. And then, the obtainedfriction coefficient is used to predict the cutting force under lubrication condition by themodified Oxley’s model. The predicted cutting force is then employed to estimate thecutting temperature based on the temperature model with considering the cooling effectof MQL. After that, the obtained cutting temperature and friction coefficient are put intothe modified Oxley’s model to predict the cutting force with considering both thelubrication and cooling effects aroused by MQL. In this way, the overall cutting forcesand temperatures are achieved through cycles of iterations between the two attributes.(3) Residual stress prediction model in MQL machining with considering boththe mechanical load and thermal loadFirst, the predicted cutting force and cutting temperature in MQL machining are putinto a Hertzian rolling and sliding contact model to predict the stress distribution inworkpiece due to both the mechanical load and thermal load. Second, the hybrid algorithm proposed by McDowell is applied to consider the arbitrary form of kinematichardening for large load ranges in MQL machining. The thermal-elastic-plastic model isutilized to predict the residual stress in MQL machining. So that, the residual stressprediction model in MQL machining is developed as a function of cutting parameters,tool geometry, material properties, and lubrication conditions.(4) Model validation based on orthogonal cutting of AISI4130alloy steelFirst, the orthogonal cutting of AISI4130alloy steel tests are performed under dry,MQL, and flood cooling machining. The cutting forces are measured by dynamometer.The cutting temperatures are measured by both thermal couple and thermal camera. Theresidual stresses along the depth of workpiece are measured by X-ray diffraction method.Second, the effects of cutting conditions on cutting force, cutting temperature, andresidual stresses are analyzed by the measured results. Third, in order to verify theproposed prediction model, comparisons are made between the prediction results and themeasured values and they show good agreements.(5) Sensitivity analysis of residual stress in MQL machiningFirst, based on the verified prediction model, the controlling factors of residualstresses in MQL machining are discussed by the principal component analysis, and themain effects of residual stresses are determined. Second, according to results of the maineffects analysis, the effects of MQL parameters, cutting parameters, and tool geometry onresidual stress distribution are discussed. The MQL parameters include the flow rate ofoil and the air-oil mixture ratio. The cutting parameters include the cutting speed, feedrate, and width of cut. The tool geometry includes the rake angle and the tool edge radius.The residual stress distribution refers to the maximum compressive residual stress and itslocation, and the average residual stress.