Abstract:
Minimum quantity lubrication (MQL) refers to the use ofcuuing fluid, of only a
minute amount typically of a flow rate of 50 to 500 ml/hour which is about three to four.
orders or magnitude l()\'er than the <IlTIount commonly u~ed in nood cooling condition.
The concept of minimum quantity lubrication (MQL) has been suggested since a decade
ago as a means of addressing the issues of environmental intrusivene,s and occupational
hazards associated with the airborne culling fluid particles on factory shop floors. This
research work deals with experimental investigation on the role of MQL by VG-68 cutting
oil on chIp thickness ratio, cutting tcmpcraturc, cUlting forces, tool wcar and surf<lce
roughness in turning medium carbon steel at industrial speed-feed combinations by
uncoatcd carbide insert and also to develop an Al1ificialNeural Network (ANN) modcl to
predict tool wear and surface roughness in a MQL environment. The encouraging reSLIlts
from expcrimcntal investigations include significant reduction in 1001 wcar rate,
dimensional inuceuraey und surface roughncss by MQL over dry lTI<lchiningmainly
through rcduction in the cutting zone temperature and favorablc chunge in the chip-tool
and work-tool interaction.
Tool wcar and tool1ife influence the productivity, quality, surfacc intcgrity, cost
and profit in any machining process. So, prediclion of tool wear and surface roughness
plays a significant rolc in indu~try for highcr produ~livity and surfocc integrity and for
JnunufaclUringproec~" planning. Artificial Neural Network (AN1':) is a very promising
tool in the field of modeling and monitoring of machining operations and for process
optimization. It can recognize pattern in the past data and base on that patlern il can act as
a pattcrn matching englnc to forecast the future data. The advantage of ANNs over
mathcmatical model is that thcy do not reqUire a precise formulation of phy~ical
relationship; they only need experimental results, In lhis study, an Artificial Neural
Network (ANN) model has been developed for prediction of tool wear and sllrfacc
roughness. The input parumeters of the ANN model are the four machining parameterscutting
speed, feed rate, dcplh of cut, machining time and thc oulput parumeters arc four
process parameters which arc principal flank wear, maximum principal flank wear,
auxiliary flank wear and surface roughness_
The proposcd model can predict tool wear and surface rollghness which is very
clo>e to experimental values. The rc>ults of the ANN model show that thc model can be
used for the optimization of the cutling process Le. culling parameters can be set in
advance prior to pcrform machining operations for cfficient and economic production and
for the purpose of manufacturing process planning in a properly designed MQL
environment.
