01-09-2016, 10:45 AM
STUDY OF SURFACE ROUGHNESS BASED ON MACHINING CONDITION AND TOOL CONDITION IN BORING EN31 STEEL
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ABSTRACT
In manufacturing industries, production of metallic materials for automobiles, aerospace and missile applications etc. required high surface finish in various components. In the present work, the effect of cutting condition like spindle speed, feed rate, depth of cut and tool flank wear on the surface roughness in machining of EN31 steel is studied. Carbide tipped insert was used for boring operation and Regression analysis was used for prediction of surface roughness. The best suitable regression model is implemented based on correlation coefficient and the error values.
Keywords: Boring process; Tool Flank Wear; EN31; Surface roughness; Regression analysis; vibration signals
INTRODUCTION
Metal cutting is one type of manufacturing process in which a sharp cutting tool edge is used to remove the material from the parent material. Boring, a process of enlarging a hole size after drilling operation over is one type of metal cutting process. The heat generated in machining depends on input parameters especially cutting speed which is most influencing factor and the type of material being machined. The main tool wear occur in boring operation is the flank wear, due to work material condition, machining conditions, tool nomenclature, surface of the work piece material, tool vibrations and work piece vibration.
Earlier researchers have contributed experimental works based on the prediction of surface roughness from the effect of machining conditions and tool wear. C. J. Rao et al. [1] investigated the influence on the interaction of feed and depth of cut and interaction of all the three cutting parameters having significant influence on cutting force, where power consumption is minimized the better surface finish was obtained. K. Venkata Roa. et al. [2] discussed the effect of cutting parameters, tool inserts, nose radius and feed rate on the surface roughness. The nose radius affects the workpiece due to the vibration, feed rate and the metal remove rate in boring AISI 1040 steel. R.S.Kadu. et al. [3] investigated the influence of effective cutting parameter to predict the surface roughness and to maximize tool life for high performance in boring operation by using cast iron. Pardeep Kumar et al. [4] optimized the cutting parameters which affect the surface roughness by increasing the speed, feed rate on reduction of surface roughness 49.83%. Srithar et al. [5] investigated that the surface roughness parameters in machining of AISI D2 steel by coated insert. The increase of cutting speed decreases the surface roughness, but while gradual increasing of feed rate and depth of cut increases the surface roughness in turning operation. Varaprasad. Bh et al.[6] Studied the effect of machining parameters like speed 165m/min, feed rate 0.05mm/rev, depth of cut 0.3 mm to achieve low tool wear 0.18 mm for better surface finish and developed a model by using ANOVA to predict the flank wear using AISI D3 steel. M. Elangovan et al. [7] studied the influence of cutting parameters for prediction of surface roughness from the vibration signals when machining EN8 steel rod regression analysis using various statistical parameters. Mahdi Danesh et al.[8] determined the cutting tool wear from the surface image of the workpiece in the turning process using un-decimated wavelet transform and the decomposition of surface image into sub-band image in the work piece. A.M. Badadhe et al[9] optimized the machining parameters will tend to reduce the machining time and increase the productivity by using Taguchi parameter design method in boring operation in EN9 steel.
2. Experimental Setup and Procedure
The work piece material used for experiments was EN31 steel. A bar of diameter 33mm x 100mm long was prepared. The chemical composition of the work piece material is given in Table.1. The work piece is end quench temperature: 820°
2.2. Statistical Features
The statistical feature is a machine learning language or toolbox provides multiple ways to explore data from the vibration signals with various cutting parameters. In regression techniques the statistical features machine learning language to study the prediction of surface roughness from the data.
The statistical analysis of vibration signals yields different parameters. The statistical parameters taken for this study are mean, standard error, median, standard deviation, sample variance, kurtosis, skewness, range, minimum, maximum and sum. Thus, the statistical features were calculated for the 200 signals acquired for each condition.
3. REGRESSION ANALYSIS
The regression analysis is a statistical procedure the relationship between the dependent variables and independent variables. It provides estimates the values of the dependent variable from values of independent variable. These estimation procedures the regression lines. The regression line X on Y is X= a+by. The regression line Yon X is Y=a+bx. The regression equation of X on Y is X-X= bxy(y-y). The regression equation of Y on X is Y-Y=byx(X-X).
Where bxy=r σx/σy, byx=r□(σy/σx). The variable we are trying to predict (Y) is called the dependent (or response) variable. The variable x is called the independent (or predictor, or explanatory) variable. In linear regression the correlation coefficient values, the two variables are treated as equals.