IntroductionOptimization of machining parameters is essential for improving expected outcome of any machining operation.Case DescriptionThe aim of this work is to find out optimum values of machining parameters to achieve minimal surface roughness during milling operation of GFRP.Discussion and EvaluationIn this machining operation speed, depth of cut and feed rate are considered as parameters affecting surface roughness and Design of Experiment (DOE)-Taguchi method tool is used to plan experiments and analyse results.ConclusionAnalysis of experimental results presents optimum values of these three parameters to achieve minimal surface roughness with speed as a major contributing factor. Speed—200 rpm, depth of cut—1.2 mm and feed—40 mm/min are an optimal combination of machining parameter to produce minimal surface roughness during milling of GFRP.

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In this study, the integrated Taguchi-simulated annealing (SA) approach is applied to examine the wear behaviour of silicon nitride (Si 3 N 4)hexagonal boron nitride (hBN). Wear tests for Si 3 N 4-hBN composite versus steel (ASTM 316L) disc were carried out for a dry sliding conditions in a so-called pin-on-disc arrangement. The tests were realized at % volume of hBN 0, 4, 8, 12, 16 in Si 3 N 4 under the loads of 5, 10, 15, 20, 25 N. The wear rate (WR) was analyzed using Taguchi-signal to noise ratio approach with the aim of finding optimal combination of load and % volume of hBN in Si 3 N 4. By applying the analysis of variance, it was also found that the greatest impact on wear rate has interaction of load and % volume of hBN with percentage effect of 51.89%, then % volume of hBN with 35.04% and load with 13.06%. The experimental results are further utelized to develop the second-order, linear mathematical model. Further, this model is processed with simulated annealing (SA) to find the optimal combination of load and % volume of hBN to minimize wear rate. Combined Taguchi-SA approach was successfully used to predict the optimal combination of load and % volume of hBN in Si 3 N 4 to minimize wear rate of Si 3 N 4. The dominant wear mechanism is adhesive wear as confirmed by scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS).

Minimizing friction and wear in between mating parts is the main concern in the field of tribological studies. Lubricants with improved tribological properties are continuously explored for minimizing friction and wear. In the present work, an attempt has been made to evaluate the effect of the addition of aluminium oxide (Al2O3) nanoparticles as lubricant additives on the tribological performance of base lubricant oil (SAE10W40) using four-ball tester. Weight in the percentage of Al2O3 nanoparticles was added in base oil and evaluated the effect of additives on wear preventive characteristic and coefficient of friction of base lubricant oil. Integrated Taguchi-Grey relational approach is implemented to obtain the optimum combination of load and % wt. of Al2O3 nanoparticle addition for improving the tribological performance of base lubricant oil. With a load of 250 N and 0.5 wt% of Al2O3 nanoparticles shows an optimum combination for the improved tribological performance of base oil. The wear scar diameter and coefficient of friction found to be reduced by 20.75 % and 22.67 % respectively with the addition of 0.5 wt% of Al2O3 nanoparticles in base lubricant oil. The lubrication performance seems to be improved because of mending effect and ball bearing effect of Al2O3 nanoparticles forming a self-protective film on the friction surface.

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