Abstract:
Reducing temperaturc is one of the key objcctives in metal cutting due to its
various detrimental cffccls On residual stress, surface even subsurface micro cracks of
work piece matcrial, dimensional devialion due to thermal softening and rapid 1001wear.
Again, less power consumption is highly desirable thul CJn be achieved by improving
lubrication at the interface zones of chip-tool and tool-work piece. Tool wear and the
surface roughlless are the ultimate outcome of the increased cultillg temperature alld force
as well. V~rious researchers worked on various techniques to effectively eonlrol Ihe
increased cutting kmperature as well U~cutting forcc, tool we~r rates, surface integrity.
The focus has been eoncenlruted on improved tool geometry design, coating and substrate
material selection of cutting tool, proper combination of machining P~fameters, proper
adoption of advJnced tcchniquc~ such as Cryo-machining, HPC (High Pressure Coolant)
machining, MQL (Minimum Quantity LlJbrication) application as a bencficial substitute of
conventional cutting fluid application. Conventional cutting fluid applicalion has severe
detrimental impacts on environment due to soil conlamination during its disposal, air
contamination during its decomposition into sulphut, phosphorus and othcr toxic gascs,
Besides, this also leads to disw;trous elTects on human hcalth who perform machining
under wet condition. In this grccn manufacturing age, considering both environmental
impact and economy of machining, HPC application has proved to be one of thc most
challenging techniques to improve machinability. Many research works have been donc so
far 10 justify its beneficilll)' cffects but predictive modeling of machining responses in
turning steel under HPC application is still a developing arena of rescareh. Thi~ paper
lllaJnly focuses on the predictive modeling of tool wcar and surface roughncss under HPC
condition while turning medium carbon sleel (AlSI 1060) at industrial speed feed
condition by uncoated carbide insert The results indicate th~t the performancc of the
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cutting tool under HPC condition is quilc g()()d and morc cf1cctivc eomplll'cd to machining
under dry condition. Based on the experimental results, predictive models of tool wcur and
surface roughness has been developed to undersland the basic phenomenon in metal
machining. Prcdiction of tool wear hJS been conducted from thc characlerization of thc
bchavior exhibited jrolll high tempcraturc and force generalion. Using statistical analysis,
predictive models ofsurfacc roughne8~ hw; been developcd. Finally, numcrical modd has
been developed to evaluate tool flank v,'car model by ABAQUSICAE, The developed
models satisfactorily validates their accuracy by comparing with desirable experimental
results.
