Influence of Minimum Quantity Lubrication Parameters on Tool Wear and Surface Roughness in Milling of Forged Steel

The minimum quantity of lubrication（MQL） technique is becoming increasingly more popular due to the safety of environment.Moreover,MQL technique not only leads to economical benefits by way of saving lubricant costs but also presents better machinability.However,the effect of MQL parameters on machining is still not clear,which needs to be overcome.In this paper,the effect of different modes of lubrication,i.e.,conventional way using flushing,dry cutting and using the minimum quantity lubrication（MQL） technique on the machinability in end milling of a forged steel（50CrMnMo）,is investigated.The influence of MQL parameters on tool wear and surface roughness is also discussed.MQL parameters include nozzle direction in relation to feed direction,nozzle elevation angle,distance from the nozzle tip to the cutting zone,lubricant flow rate and air pressure.The investigation results show that MQL technique lowers the tool wear and surface roughness values compared with that of conventional flood cutting fluid supply and dry cutting conditions.Based on the investigations of chip morphology and color,MQL technique reduces the cutting temperature to some extent.The relative nozzle-feed position at 120°,the angle elevation of 60° and distance from nozzle tip to cutting zone at 20 mm provide the prolonged tool life and reduced surface roughness values.This fact is due to the oil mists can penetrate in the inner zones of the tool edges in a very efficient way.Improvement in tool life and surface finish could be achieved utilizing higher oil flow rate and higher compressed air pressure.Moreover,oil flow rate increased from 43.8 mL？h to 58.4 mL？h leads to a small decrease of flank wear,but it is not very significant.The results obtained in this paper can be used to determine optimal conditions for milling of forged steel under MQL conditions.

Prediction of product roughness,profile,and roundness using machine learning techniques for a hard turning process

High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control(CNC)machining.Quality monitoring and prediction is of great importance to assure high-quality or zero defect production.In this work,we consider roughness parameter Ra,profile deviation Pt and roundness deviation RONt of the machined products by a lathe.Intrinsically,these three parameters are much related to the machine spindle parameters of preload,temperature,and rotations per minute(RPMs),while in this paper,spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters.Power spectral density(PSD)based feature extraction,the method to generate compact and well-correlated features,is proposed in details in this paper.Using the efficient features,neural network based machine learning technique turns out to be able to result in high prediction accuracy with R2 score of 0.92 for roughness,0.86 for profile,and 0.95 for roundness.