Coimbatore, INDIA. ABSTRACT. The aim of this work is to utilize taguchi method to investigate the effects of drilling parameters such as cutting speed (5,.
International Journal of Advanced Engineering Research and Studies
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Research Paper
EFFECT OF DRILLING PARAMETERS ON SURFACE ROUGHNESS, TOOL WEAR, MATERIAL REMOVAL RATE AND HOLE DIAMETER ERROR IN DRILLING OF OHNS J.Pradeep Kumar1*, P.Packiaraj2 Address for Correspondence 1* Assistant Professor, 2 PG Student, Department of Production Engineering PSG College of Technology Coimbatore, INDIA ABSTRACT The aim of this work is to utilize taguchi method to investigate the effects of drilling parameters such as cutting speed (5, 6.5, 8 m/min), feed (0.15, 0.20, 0.25mm/rev) and drill tool diameter (10, 12, 15mm) on surface roughness, tool wear by weight, material removal rate and hole diameter error in drilling of OHNS material using HSS spiral drill. Orthogonal arrays of taguchi, the Signal–to- Noise (S/N) ratio, the analysis of variance (ANOVA), and regression analysis are employed to analyze the effect of drilling parameters on the quality of drilled holes. A series of experiments based on L18 orthogonal array are conducted using DECKEL MAHO-DMC 835V machining center .The experimental results are collected and analyzed using commercial software package MINITAB 13. Linear regression equations are developed with an objective to establish a correlation between the selected drilling parameters with the quality characteristics of the drilled holes. The predicted values are compared with experimental data and are found to be in good agreement. KEYWORDS: Drilling, OHNS, Taguchi method, Surface finish, Tool wear, Material removal rate, Hole diameter error, Regression analysis, Analysis of variance.
1. INTRODUCTION The largest amount of money spent on any one class of cutting tools is spent on drills. Therefore, from the viewpoint of cost and productivity, modeling and optimization of drilling processes are extremely important for the manufacturing industry (S.A. Jalali et.al 1991). Amongst traditional machining processes, drilling is one of the most important metal cutting operations, comprising 33% of all metal cutting operations (Chen W C et.al, 1999). Although modern metal cutting methods have tremendously improved in the manufacturing industry, conventional drilling process still remains one of the most common processes. Taguchi design is proved to be an efficient tool to produce high quality products at very less cost. The objective of taguchi robust design is to determine the optimal parameter settings and making the process performance insensitive to various sources of variations. The approach can economically satisfy the needs of the problem solving and design optimization. Taguchi technique allows the process optimization with minimum number of experiments without need for process model development. Thus, by this method, it is possible to reduce the time and cost for experimental investigations and thus enhance the performance characteristics. The material chosen for the study is Oil hardened Non Shrinking (OHNS) steel which comes under tool steel is widely used in manufacturing tools such as machine screw taps, threading dies, intricate press tools, chasers and milling cutters. The percentage of carbon and its alloys in OHNS steel are: Carbon 0.95%, Chromium 0.5%, V 0.15%, W 0.6% and the rest is iron. Studying about machining of this steel using drilling process will be useful for manufacturing industries. Drilling operation is evaluated based on the performance characteristics such as surface roughness, material removal rate (MRR), tool wear, tool life, cutting force, hole diameter error, power consumption and are strongly correlated with the cutting parameters such as cutting speed, feed, depth of cut, and tool geometry (Chryssolouris G et.al, 1990, Chua MS et.al, 1993, Yang H et.al,1998, Paulo IJAERS/Vol. I/ Issue III/April-June, 2012/150-154
davim.J et.al,2003) ,which are determined based on experience or by the use of a handbook (Graham T et.al, 2008). In this context, three parameters speed, feed and drill tool diameter are selected as controllable parameters and parameters like surface roughness(Ra), MRR, tool wear by weight and hole diameter error are considered as the required quality characteristic responses. Several mathematical models based on statistical regression and neural network techniques have been constructed to establish the relationship between the cutting performance and cutting parameters. Han-Ming Chow ct.al, 2008 has explored how different parameters such as drill shape, friction angle, friction contact area ratio, feed rate, and drilling speed would affect the response parameter for austenite stainless steel (AISI 304) using taguchi method. Routio et.al, 1995 studied the tool wear and failure in the drilling of stainless steels. J Paulo davim, 2003 presented a study on the influence of cutting parameters such as cutting velocity, feed rate, cutting time on drilling metal – matrix composites and concluded that interaction of cutting speed/feed is the most important factor contributing towards surface roughness of drilled holes. C.C Tsao, 2004 performed an experimental work with an objective to establish a correlation between feed rate, spindle speed and drill diameter with the induced delamination in a CFRP laminate. Mohan et.al, 2005 used taguchi method to study the influence of process parameters such as speed, feed rate, drill size, specimen thickness on cutting force and torque during drilling of glass fiber polyester reinforced composites. Basavarajappa et.al, 2008 discussed the influence of speed and feed on drilling of hybrid metal matrix composites based on taguchi techniques. Mustafa et.al 2009 performed an experimental investigation in the optimization of cutting parameters for surface roughness in dry drilling process using taguchi method. It is evident from literature survey that there exists a need to study on the effects of various parameters on drilling of OHNS since lot of works are reported pertaining to drilling of composite materials, aluminum alloys and very rarely on drilling of tool steels.
International Journal of Advanced Engineering Research and Studies 2. Experimental works. In this study, the settings of drilling parameters were determined by using taguchi experimental design method. Orthogonal arrays of taguchi, the Signal – to – Noise (S/N) ratio, the analysis of variance (ANOVA), and regression analysis are employed to analyze the effect of the drilling parameters on surface roughness, tool wear by weight, material removal rate (MRR) and hole diameter error values. In order to reduce time and cost, experiments are carried out using L18 orthogonal array. For the purpose of observing the degree of influence of the cutting conditions in drilling process three factors (cutting speed, feed and drill diameter), each at three levels are taken into account as shown in Table 1 and 2. 2.1 Experimental set up Drilling tests are performed on DECKEL MAHODMC 835V (continues speed up to 14000rpm and 14kw spindle power) CNC machining centre using HSS spiral type drill bits. To guarantee the initial conditions of each test, a new drill tool is used in each experiment. The work piece is of OHNS material with dimensions 300 x 100 x 10 mm. The experimental setup is shown
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roughness is an average taken from eight measurements along the holes. In these experiments, the final shape of the hole is determined using a Renishaw cyclone CMM. The measurement of hole diameter is of critical importance for many applications. One of the most important fundamental factors for engineering components is precision assembly. Hence, in this study, the hole diameter error of a produced hole is measured using a Renishaw cyclone CMM. The major system components are the three-axis mechanical setup, the probe head, control unit, and PC. The CMM used here is a vertical-arm CMM, using a Renishaw PH sensor mount with a touch-trigger probe. The operating system utilized is Windows for PCs.
Figure 2. Set up for measuring Hole diameter error The setup used for measuring hole diameter error is shown in figure 2 and the experimental results are shown in table 2.The drill tool weight comparison before and after machining is taken as a measure of tool wears. The tool wear is measured using the shimadzu electronic balance machine. The material removal rate in drilling of OHNS plate has been calculated by using the standard relation: d2*f*N) mm3/min (1) MRR= ( Where,
Figure 1. Experimental setup in figure 1.CNC part programs are created by employing MasterCam 10 CAD/CAM software on a MRR= Material removal rate, d = diameter of the drill bit in mm, f = feed in mm/rev, N = spindle speed in rev/min. personal computer. The mean surface roughness (Ra) The measured values of surface roughness, tool wear, is measured with a Mitutoyo Surftest SJ-201 Series hole diameter error and calculated values of material 178-portable surface roughness tester instrument. The removal rate are shown in Table 2. cut –off and sampling lengths for each measurement are taken as 0.8 and 5mm, respectively. The surface Table 1. Process parameters and their levels
Table 2. L18 orthogonal array and the desired parameter values
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International Journal of Advanced Engineering Research and Studies 3. Analysis of experimental results and discussion 3.1 Regression Analysis The cutting speed, feed and drill tool diamter are considered in the development of mathematical models for surface rougness, toolwear, metal removal rate and hole diameter accuracy.The correlation between the considered drilling paramters for drilling conditions on OHNS are obtaine by linear regression. The linear polynomial models are developed using commercially available Minitab 13 software for various drilling paramters and are listed as below: Surface Roughness= 0.81+0.056A+25.6B-0.290C (2) Tool Wear= 3.05-0.0914A+3.47B-0.176C (3) Material Removal Rate = -7988+611A+19934B+326C (4) Hole Diameter Error= - 0.0176 - 0.00109A + 0.112 B + 0.00265C (5) Where: A = Cutting speed (m/mm), B = Feed (mm/rev), C = Drill diameter (mm).
The predicated or theoretical values of various parameters obtained using regression equations are shown in table 2. The predicted values are also compared with experimental values and shown in figure 3.
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3.2 Analysis of S/N ratio In the taguchi method, the term signal represents the desirable value (mean) for the output characteristic and the term noise represents the undesirable value (deviation, SD) for the output characteristic. Therefore the S/N ratio is the ratio of the mean to the SD. Taguchi uses the S/N ratio to measure the quality characteristic deviating form the desired value. There are several S/N ratios available, depending on the type of the characteristic; lower the better, nominal is best and higher the better.(Phadke MS(1989), Philip J Ross (1996). The lower the better characteristic (Eq.6) is used for surface roughness, tool wear and hole diameter error and higher the better (Eq.7) is used for Metal removal rate and both the characteristics are formulated as shown below Smaller the Better : (6) S/NL= Higher the Better : S/NH=
(7)
Where , is the average of observed data, is the variance of y, n is the number of observations and y is the observed data. Using the above-presented data with the selected above formula for calculating S/N, the Taguchi experiment results are summarized in Table 3,4,5,6 and presented in Fig. 4,5.6.7, which were obtained by means of MINITAB 13 statistical software. It can be noticed form the S/N responses that feed is the most important factor affecting surface roughness, tool wear, metal removal rate and drill tool diameter is the most important factor affecting the hole diameter accuracy. Fig 4,5,6,7 shows the effect of drilling parameters on surface roughness, tool wear, metal removal rate and hole diameter error respectively. Table 3. Responses for Signal-to-Noise ratio (S/N) – Surface Roughness
Table 4. Responses for Signal-to-Noise ratio (S/N) – Tool Wear
Table 5. Responses for Signal-to-Noise ratio (S/N) – Metal Removal Rate
Table 6. Responses for Signal-to-Noise ratio (S/N) – Hole Diameter Error
Figure 3. Comparison of experimental values and predicted values IJAERS/Vol. I/ Issue III/April-June, 2012/150-154
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Fig 4. Main effect plot for Surface Roughness
Fig 5. Main effect plot for Tool Wear
Fig 6. Main effect plot for Material removal rate
Fig 7. Main effect plot for Hole dimater error 3.3 Analysis of variance (ANOVA) The analysis of variance (ANOVA) establishes the relative significance of factors in terms of their percentage contribution to the response (Phadke, 1989; Ross, 1996) The ANOVA is also needed for estimating the variance of error for the effects and the confidence interval of the prediction error. The analysis is performed on S/N ratios to obtain the percentage contribution of each of the factors. DoF: Degree of freedom, SS: Sum of squares, %c: Percent contribution, #: 95% confidence interval IJAERS/Vol. I/ Issue III/April-June, 2012/150-154
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From table 7, observation can be made such that the feed (62.24%) have greater influence on surface roughness and drill tool diameter, cutting speed accounts 13.94%, 11.48% percent contribution respectively on the holes surface roughness. From table 8, observation can be made such that the feed (53.93%) have greater influence on tool wear and, cutting speed and drill tool diameter accounts 21.48%, 5.88% percent contribution respectively on tool wear. From table 9, observation can be made such that the feed (39.21%) have greater influence on MRR and cutting speed and drill toll diameter accounts 32.87%, 25.36% percent contribution respectively on MRR. From table 10, observation can be made such that the feed (83.38%) have greater influence on hole diameter error and cutting speed and drill toll diameter accounts 1.17%, 0.50% percent contribution respectively on the hole diameter error. 4. CONCLUSIONS This study has discussed an application of taguchi method for investigating the effects of drilling parameters on surface roughness, tool wear, material removal rate and hole diameter error in drilling of OHNS. From the analysis of results in the drilling process using conceptual Signal-to-Noise(S/N) ratio approach, regression analysis, analysis of variance(ANOVA) the following can be concluded from the present study: • Statistically designed experiments based on taguchi method are performed using L18 orthogonal array to analyze the effect of drilling parameters on surface roughness, tool wear, material removal rate and hole diameter error. • Linear regression equations are developed to predict the values of surface roughness, tool wear, material removal rate and hole diameter error and the predicted values are compared with measured values. • Through ANOVA, it is found that the feed and speed are important process parameters to control surface roughness, tool wear, material removal rate and hole diameter error. • Thus it is essential to employ suitable combination of cutting speed and feed so as to reduce the variations that can affect the quality of the holes that are drilled on OHNS material. • Further study could consider more factors (drill properties [point angle, helix angle, flute number, types of drills] and run out of drill, thrust force, toques etc.) in the research to see how these factors would affect the hole quality. ACKNOWLEDGEMENTS The authors express their sincere thanks to the Management, Principal PSG College of Technology, Dr.K.Prakasan – Professor and Head, Department of Production Engineering, PSG College of technology for providing necessary support and infrastructure to carry out this work. REFERENCES 1.
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