Your SlideShare is downloading. ×
  • Like
20120140503002
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply
Published

 

Published in Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
98
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
1
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 9 OPTIMIZATION OF END MILLING PARAMETERS OF AISI 1055 BY TAGUCHI METHOD Kareem Idan Fadheel Ministry of Higher Education & Scientific Research, Foundation of Technical Education, KUT Technical Institutes, Republic of Iraq Dr. Mohammad Tariq Department of Mechanical Engineering, Shepherd School of Engineering and Technology, SHIATS-DU, Naini, Allahabad-211007 ABSTRACT CNC End milling is a unique adaption of the conventional milling process which uses an end mill tool for the machining process. During the End milling process, the material is removed by the end mill cutter. Surface finish is an important indicator of the milling operation in manufacturing process. This paper, aims to optimize milling parameters depend on the Taguchi method for minimizing surface roughness (Ra). The experiments were conducted using the L18 orthogonal array in a CNC milling machine with respect to three different cutting parameters. These parameters were cutting depth, cutting speed and feed rate. Dry milling tests were performed on hardened AISI 1055 (48HRC) with APTK 1705 LT 30 carbide inserts as a cutting condition in order to reduce optimization parameters such as the coolant. Each experiment was repeated five times and a new insert was used for each test to ensure accurate readings of surface roughness. In addition, the statistical methods of signal to noise ratio (S/N) and analysis of variance (ANOVA) were carried out to investigate the effects of cutting parameters on surface roughness. As a result of the experiment, the feed rate has the most dominant effect on average surface roughness. The effects of interactions of factors appear to be important, especially cutting speed-feed rate pair. These statistical methods can be very useful in manufacturing units to optimize the manufacturing processes. Keywords: Taguchi Method, End Milling, S/N Ratio, Surface Roughness, ANOVA. INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 3, March (2014), pp. 09-20 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  • 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 10 1. INTRODUCTION Milling operates on the principle of rotary motion. A milling cutter is spun about an axis while a workpiece is advanced through it in such a way that the blades of the cutter are able to shave chips of material with each pass. Milling processes are designed such that the cutter makes many individual cuts on the material in a single run; this may be accomplished by using a cutter with many teeth, spinning the cutter at high speed, or advancing the material through the cutter slowly. Most often it is some combination of the three [2]. The speed at which the piece advances through the cutter is called feed rate, or just feed; it is most often measured in length of material per full revolution of the cutter. As material passes through the cutting area of a milling machine, the blades of the cutter take swarfs of material at regular intervals. This non-continuous cutting operation means that no surface cut by a milling machine will ever be completely smooth; at a very close level (microscopic for very fine feed rates), it will always contain regular ridges. These ridges are known as revolution marks, because rather than being caused by the individual teeth of the cutter, they are caused by irregularities present in the cutter and milling machine; these irregularities amount to the cutter being at effectively different heights above the workpiece at each point in its rotation. The height and occurrence of these ridges can be calculated from the diameter of the cutter and the feed [4]. These revolution ridges create the roughness associated with surface finish. Robust design is an engineering methodology for obtaining product and process conditions, which are minimally sensitive to the various causes of variation to produce high-quality products with low development and manufacturing costs [12]. Taguchi’s parameter design is an important tool for robust design. It offers a simple and systematic approach to optimize design for performance, quality and cost. Two major tools used in robust design are [12–14]: signal to noise ratio, which measures quality with emphasis on variation Orthogonal arrays, which accommodate many design factors simultaneously. The successful applications of Taguchi methods by both engineers and statisticians within British industry have lead to the formation of UK Taguchi Club [15]. Taguchi’s approach is totally based on statistical design of experiments [12], and this can economically satisfy the needs of problem solving and product/process design optimization [16]. By applying this technique one can significantly reduce the time required for experimental investigation, as it is effective in investigating the effects of multiple factors on performance as well as to study the influence of individual factors to determine which factor has more influence, which less [12,16]. Some of the previous works that used the Taguchi method as tool for design of experiment in various areas including metal cutting are listed in [17–23] references. 1.1 Taguchi methods Taguchi methods are statistical methods developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to engineering [1], biotechnology [2, 3], marketing and advertising [4]. Professional statisticians have welcomed the goals and improvements brought about by Taguchi methods, particularly by Taguchi's development of designs for studying variation, but have criticized the inefficiency of some of Taguchi's proposals [5]. Taguchi’s work includes three principal contributions to statistics: A specific loss function The philosophy of off-line quality control Innovations in the design of experiments
  • 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 1.2 Design of Experiments Taguchi developed his experimental theories independently. Taguchi read works following R. A. Fisher only in 1954. Taguchi's framework for design of experiments flawed, but contains much that is of enormous value. sequential designs of response surface methodology a sequence of Taguchi's designs [8]. 2. METHODOLOGY The design of parameters in the Taguchi method aims to determine the parameters generating the best levels of optimum cutting conditions. To find the optimum cutting conditions, the Taguchi method uses the signal to noise ratio (S/N) via an orthogonal arra value. The signal to noise ratio characteristics can be calculated in three different ways; • Smaller the better • The larger the better • The nominal-the best The equations of the three levels are given Smaller the better Larger-the better Nominal the best where n is repeat number of experiment and is measured variable value i average of observed data, the variance of data. For each type of the characteristics, with the above S/N ratio transformation, the higher the S/N ratio the better is the result. The optimum cutting parameters required for the best surface roughness were obtained by using eq.1 the smaller the better. By using the smaller levels were estimated. S/N ratios according to the mentioned e paragraphs. Fig 1: Shear length and shear angle in chip formation process International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 11 Taguchi developed his experimental theories independently. Taguchi read works following R. only in 1954. Taguchi's framework for design of experiments is idiosyncrat flawed, but contains much that is of enormous value. He made a number of innovations. sequential designs of response surface methodology require far fewer experimental runs than would The design of parameters in the Taguchi method aims to determine the parameters generating the best levels of optimum cutting conditions. To find the optimum cutting conditions, the Taguchi method uses the signal to noise ratio (S/N) via an orthogonal array. S/N ratio is used as a measurable value. The signal to noise ratio characteristics can be calculated in three different ways; The equations of the three levels are given by eq. (1-3). Smaller the better the better where n is repeat number of experiment and is measured variable value in equ the variance of y, n the number of observations, and data. For each type of the characteristics, with the above S/N ratio transformation, the higher the S/N The optimum cutting parameters required for the best surface roughness were obtained by using eq.1 the smaller the better. By using the smaller-the better (eq.1), S/N ratios of parameters and levels were estimated. S/N ratios according to the mentioned equations are given in following Shear length and shear angle in chip formation process International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – , © IAEME Taguchi developed his experimental theories independently. Taguchi read works following R. is idiosyncratic and often He made a number of innovations. The require far fewer experimental runs than would The design of parameters in the Taguchi method aims to determine the parameters generating the best levels of optimum cutting conditions. To find the optimum cutting conditions, the Taguchi y. S/N ratio is used as a measurable value. The signal to noise ratio characteristics can be calculated in three different ways; (1) (2) (3) n equation is the the number of observations, and y the observed data. For each type of the characteristics, with the above S/N ratio transformation, the higher the S/N The optimum cutting parameters required for the best surface roughness were obtained by the better (eq.1), S/N ratios of parameters and quations are given in following
  • 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue The chip formation process is influenced by the shear length (ls) is given as: where t is undeformed chip thickness and shear angle ∅ is large at high cutting speeds, therefore the shear length figure 1. End milling is aim to removing material work pieces. Basically the tool has rotating motion (spindle speed) and the work pieces is linear ones (feed rate) as shown in fig. 2. Fig. 2.1 Experimental design Response surface methodology (RSM) is a procedure which is able to determine a relationship between independent input process parameters (e.g. cutting parameters) and output data (process response, e.g. Ra). In the current study, the relationship between the input pa cutting conditions (cutting speed (vc parameters, defined as the machinability aspect ( Ra= (vc, f, t) where is the response function. The approximation of Ra is proposed by using the following equation which consists of linear and quadratic effects of the input parameters and their interactions as well: International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 12 The chip formation process is influenced by the shear length (ls) in the shear zone. The shear length is undeformed chip thickness and is the shear angle [25]. Philip [ is large at high cutting speeds, therefore the shear length (ls) is small, as shown in End milling is aim to removing material by two continuous motions. Those are the tool and work pieces. Basically the tool has rotating motion (spindle speed) and the work pieces is linear ones Fig. 2: End milling operation rface methodology (RSM) is a procedure which is able to determine a relationship between independent input process parameters (e.g. cutting parameters) and output data ). In the current study, the relationship between the input pa c, m/min), feed rate (f, mm), depth of cut (t, mm), and the output parameters, defined as the machinability aspect (Ra) which is given as: is proposed by using the following equation which consists of linear and quadratic effects of the input parameters and their interactions as well: International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – , © IAEME in the shear zone. The shear length (4) Philip [24] found the is small, as shown in by two continuous motions. Those are the tool and work pieces. Basically the tool has rotating motion (spindle speed) and the work pieces is linear ones rface methodology (RSM) is a procedure which is able to determine a relationship between independent input process parameters (e.g. cutting parameters) and output data ). In the current study, the relationship between the input parameters, as the , mm), and the output (5) is proposed by using the following equation which consists of linear
  • 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 13 Rୟ ൌ b଴ ൅ bଵvୡ ൅ bଶf ൅ bଷt ൅ bଵଵvୡ ଶ ൅ bଶଶfଶ ൅ bଷଷtଶ ൅ bଵଶvୡf ൅ bଵଷvୡt ൅ bଶଷftɛ (6) where bis are the calculated coefficients, vc, f and t are input parameters, and ε is the experimental error. Chemical composition of AISI 1055 Table 1: Chemical Composition of the Alloy Steel AISI 1055 S. No. Constituents Chemical Composition Limits, (%) 1 C 0.5 – 0.6 2 Mn 0.6 – 0.9 3 P 0.04 (Max.) 4 S 0.05 (Max.) Table 2: Estimated Physical Properties of Hot Rolled Carbon Steel Bars S. No. Properties Estimated Minimum Values 1 Tensile Strength (psi) 94,000 2 Yield Strength (psi) 51,500 3 Elongation In 2in., (%) 12 4 Reduction in Area, (%) 30 5 Brinell Hardness 192 2.2 Experimental Procedure In order to achieve the correlation between cutting parameters and surface roughness, different parameters were used in the experiments. As work material, hardened AISI 1055 (200x50x50 mm) steel was used. The hardness of AISI 1055 steel was increased by applying heat treatment and measured hardness of AISI 1055 steel was 48 HRC. Hardened AISI 1055 steel parts were machined with KAFU CNC milling machine. The samples were prepared by cutting all surfaces at 2 mm depth of cut with the milling process to obtain clear surface for measurement and their sizes were fixed to 50x50x40 mm dimension because of the dimensional differences of work part size at the end of heat treatment. The experiment was performed under dry conditions. The tool holder and insert were used as 5 slots RILT730-M-W-D2500/5 and APTK 1705 LT30 respectively. The measurement of the average surface roughness (Ra) on surface of hardened AISI 1055 was taken by Mitutoyo SJ – 301P portable device within the sampling length of 250 mm and the measurements. The levels of cutting parameters; depth of cut (a), feed rate (f) and cutting speed (v) were chosen from the insert manufacturer’s catalogue in accordance with recommended test values. The cutting parameters were given in Table 3. The Taguchi and variance of analysis were carried out to reduce the number of the experiments and optimize cutting parameters. 3. RESULTS AND DISCUSSION The experiment has done to optimize the milling parameters to get better (i.e. low value) surface roughness; the smaller the better characteristics are used. Table 4 shows the actual data for surface roughness and corresponding computed S/N ratio for these parameters. Whereas table 5, 6, 7 and 8 show the mean S/N ratio for each levels of surface roughness for various parameters (A, B and C) and their one interaction namely BXC. Fig 3 shows the mean S/N ratio for various parameters of surface roughness.
  • 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 14 Table 3: Input parameters taken for calculations Factor Level 0 1 2 A- Cutting Speed (vc, m/min) 250 300 350 B - Feed (f, mm/tooth) 0.10 0.20 0.30 C - Radial Depth of Cut (t, mm) 0.25 0.50 0.75 Table 4: Experimental results for Surface roughness and their S/N ratio Exp. Run Factor Designation Measured Parameters S/N Ratio A B C Surface roughness Ra (µm) 1 0 0 0 A଴B଴C଴ 0.186 14.609 2 0 1 1 A଴BଵCଵ 0.175 15.139 3 0 2 2 A଴BଶCଶ 0.432 7.290 4 1 0 0 AଵB଴C଴ 0.296 10.574 5 1 1 1 AଵBଵCଵ 0.301 10.420 6 1 2 2 AଵBଶCଶ 0.317 9.972 7 2 0 0 AଶB଴C଴ 0.571 4.861 8 2 1 1 AଶBଵCଵ 0.586 4.642 9 2 2 2 AଶBଶCଶ 0.606 4.350 10 0 0 1 A଴B଴Cଵ 0.282 10.995 11 0 1 2 A଴BଵCଶ 0.506 5.916 12 0 2 0 A଴BଶC଴ 0.464 6.669 13 1 0 1 AଵB଴Cଵ 0.243 12.280 14 1 1 2 AଵBଵCଶ 0.726 2.786 15 1 2 0 AଵBଶC଴ 0.281 11.025 16 2 0 1 AଶB଴Cଵ 0.311 10.144 17 2 1 2 AଶBଵCଶ 0.652 3.715 18 2 2 0 AଶBଶC଴ 0.693 3.185 19 0 0 2 A଴B଴Cଶ 1.121 -0.992 20 0 1 0 A଴BଵC଴ 0.812 1.808 21 0 2 1 A଴BଶCଵ 0.923 0.695 22 1 0 2 AଵB଴Cଶ 1.552 -3.817 23 1 1 0 AଵBଵC଴ 0.912 0.800 24 1 2 1 AଵBଶCଵ 0.920 0.724 25 2 0 2 AଶB଴Cଶ 1.461 -3.290 26 2 1 0 AଶB଴Cଶ 1.217 -1.705 27 2 2 1 AଶBଶCଵ 1.338 -2.529 It is clearly indicated from the table 4.2 that the S/N ratio has very limited variation irrespective to the actual values of surface roughness.
  • 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 15 Table 5: Experimental results for Mean S/N ratio for cutting speed at different levels for Surface roughness S. No. Calculated S/N Ratio Calculated S/N Ratio Calculated S/N Ratio Cutting Speed (A) Level 0 Cutting Speed (A) Level 1 Cutting Speed (A) Level 2 1 14.609 10.574 4.861 2 15.139 10.420 4.642 3 7.290 9.972 4.350 4 10.574 12.280 10.144 5 5.916 2.786 3.715 6 6.669 11.025 3.185 7 -0.992 -3.817 -3.290 8 1.808 0.800 -1.705 9 0.695 0.724 -2.529 Mean S/N Ratio 6.856 6.76 2.59 Table 6: Experimental results for Mean S/N ratio for feed rate at different levels for Surface Roughness S. No. Calculated S/N Ratio Calculated S/N Ratio Calculated S/N Ratio Feed Rate (B) Level 0 Feed Rate (B) Level 1 Feed Rate (B) Level 2 1 14.609 15.139 7.290 2 10.574 10.420 9.972 3 4.861 4.642 4.350 4 10.995 5.916 6.669 5 12.280 2.786 11.025 6 10.144 3.715 3.185 7 -0.992 1.808 0.695 8 -3.817 0.800 0.724 9 -3.290 -1.705 -2.529 Mean S/N Ratio 6.151 4.835 4.597 Table 7: Experimental results for Mean S/N ratio for Depth of Cut at different levels for Surface Roughness S. No. Calculated S/N Ratio Calculated S/N Ratio Calculated S/N Ratio Depth of Cut (C) Level 0 Depth of Cut (C) Level 1 Depth of Cut (C) Level 2 1 14.609 15.139 7.290 2 10.574 10.420 9.972 3 4.861 4.642 4.350 4 6.669 10.995 5.916 5 11.025 12.280 2.786 6 3.185 10.144 3.715 7 1.808 0.695 -0.992 8 0.800 0.724 -3.817 9 -1.705 -2.529 -3.290 Mean S/N Ratio 5.758 6.945 2.881
  • 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 16 Table 8: Experimental results for Mean S/N ratio for interaction (BXC) at different levels for Surface Roughness S. No. Calculated S/N Ratio Calculated S/N Ratio Calculated S/N Ratio Interaction (B⨉⨉⨉⨉C) Level 0 Interaction (B⨉⨉⨉⨉C) Level 1 Interaction (B⨉⨉⨉⨉C) Level 2 1 14.609 10.995 -0.992 2 15.139 5.916 1.808 3 7.290 6.669 0.695 4 10.574 12.280 -3.817 5 10.420 2.786 0.800 6 9.972 11.025 0.724 7 4.861 10.144 -3.290 8 4.642 3.715 -1.705 9 4.350 3.185 -2.529 Mean S/N Ratio 9.095 7.412 -0.922 Fig 3: Mean S/N ratio for various parameters of Surface roughness Table 9: Response table for average S/N ratio for surface roughness factors and significant interaction for Surface Roughness Cutting Parameters Max-Min Net Value Cutting Speed (A) 6.856-2.59 4.266 Feed Rate (B) 6.151-4.597 1.554 Depth of Cut (C) 6.945-2.881 4.064 Interaction (B⨉C) 9.095– (-0.922) 10.017
  • 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 17 4. DESIGN OF EXPERIMENT In this experiment with three factors at three levels each, the fractional factorial design used is a standard L27 (313 ) orthogonal array [12]. This orthogonal array is chosen due to its capability to check the interactions among factors. Each row of the matrix represents one trial. However, the sequence in which these trials are carried out is randomized. The three levels of each factor are represented by a ‘0’ or a ‘1’ or a ‘2’ in the matrix. Factors A, B, and C are arranged in columns 2, 5 and 6, respectively, in the standard L27 (313 ) orthogonal array as shown in Appendix A. 4.1 Pareto ANOVA Analyses One of the methods to analyze data for process optimization is the use of Pareto ANOVA. Pareto ANOVA is a simplified ANOVA method which uses Pareto principles. It is a quick and easy method to analyze results of parameter design. It does not require an ANOVA table and therefore does not use F-tests. Following are the Pareto ANOVA table for surface roughness analysis. The Pareto ANOVA technique of analysis has been performed, which requires least knowledge about ANOVA method and suitable for engineers and industrial practitioners. The use of S/N ratio for selecting the best levels combination for surface roughness suggests that cutting speed (factor A) and interaction B×C have strong effect on the surface roughness. From the result obtained, the best combination to get low value of surface roughness is at level ‘2’ of cutting speed, level ‘0’ of feed rate, and level ‘1’ of depth of cut. Since the role of depth of cut is least in obtaining good surface finish, it is indicated that in order to achieve good surface finish, always use high cutting speed and low feed rate. By increasing the cutting speed, surface roughness values are kept at minimum. Table 10: Pareto ANOVA analysis for surface roughness Sum of Factor Level Factor and interaction BXC A B C BXC AXB AXC AXB AXC 0 81.857 61.434 55.364 51.826 41.351 52.985 44.284 43.624 51.285 1 66.715 54.764 43.521 62.51 42.981 46.906 54.003 56.299 42.111 2 -8.306 25.902 41.381 25.93 55.934 40.375 41.979 40.343 46.87 Sum of Squares of differences (S) 13986.796 2140.027 340.36 2122.846 383.1 238.62 244.348 450.567 126.302 Contribution ratio (%) 69.818 10.682 1.698 10.596 1.912 1.191 1.219 2.249 0.630 1 2 3 4 5 6 7 8 9 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 AXCAXBAXCBBXCAXB CA BXC Contributionratio(%) Factors and Interactions Values in (%) Fig 4: Pareto diagram for surface roughness From the table 10, the optimum combination of significant factor level is A0B0C1 (maximum) for surface roughness. Figure 4 shows the maximum value to (BXC) for surface roughness.
  • 10. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 18 Table 11: Calculated BXC two way tables for surface roughness Factor level B0 B1 B2 TOTAL C0 30.044 0.903 20.879 51.826 C1 33.419 30.201 -1.11 62.51 C2 -8.099 12.417 21.612 25.93 TOTAL 55.364 43.521 41.381 140.266 From the BC two way table 11, B0C1 (max) is found to be an optimal condition for surface roughness. 4.2 Discussion In the above experimental results, two techniques of data analysis have been used. Both techniques draw similar conclusions. The cutting speed has found to be the most significant effect to produce low value of average surface roughness (Ra). The explanation for the influence of cutting speed on surface finish is still not available. This could be explained in terms of the velocity of chips that is faster at high cutting speed than at low cutting speed. This leads to a shorter time for the chips to be in contact with the newly formed surface of workpiece and the tendency for the chips to wrap back to the new face form is little as compared to low speed. The condition of seizure and sub layer plastic flow occurred at high speed and the term flow-zone is used to describe secondary deformation in this range [27]. The time taken for the chips at this flow-zone for high speed cutting is short as compared to lower speed, as the velocity of chip is faster. The use of S/N ratio for selecting the best levels of combination for surface roughness (Ra) value suggests the use of low value of feed rate in order to obtain good finish. Smaller angle of tool angular position is obtained at lower depth of cut [28]. Therefore, it is preferable to set the depth of cut to a low value. Therefore, one can say that the set values for level ‘0’ and ‘1’ are both suitable to obtain good quality of surface finish. From the result, the interaction of factor B and factor C is more important than the effect of the individual factors. In other words, in order to get the best result it requires experience to combine these two factors to achieve a suitable combination of feed rate and depth of cut. 4.3 Conclusion This paper illustrates the application of the parameter design (Taguchi method) in the optimization of end milling operation. The following conclusions can be drawn based on the above experimental results of this study: Taguchi’s Method of parameter design can be performed with lesser number of experimentations as compared to that of full factorial analysis and yields similar results. Taguchi’s method can be applied for analyzing any other kind of problems as described in this paper. It is found that the parameter design of the Taguchi method provides a simple, systematic, and efficient methodology for optimizing the process parameters REFERENCE 1. Rosa, Jorge Luiz ; ROBIN, Alain ; SILVA, M. B. ; BALDAN, Carlos Alberto ; PERES, Mauro Pedro. Electro deposition of copper on titanium wires: Taguchi experimental design approach. Journal of Materials Processing Technology, Vol. 209, p. 1181-1188, 2009. 2. Rao, Ravella Sreenivas; C. Ganesh Kumar, R. Shetty Prakasham, Phil J. Hobbs (2008). “The Taguchi methodology as a statistical tool for biotechnological applications: A critical appraisal”, Biotechnology Journal 3 (4): 510–523.
  • 11. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 19 3. Rao, R. Sreenivas; R.S. Prakasham, K. Krishna Prasad, S. Rajesham, P.N. Sarma, L. Venkateswar Rao (April 2004). "Xylitol production by Candida sp.: parameter optimization using Taguchi approach". Process Biochemistry 39 (8): 951–956. 4. Selden, Paul H. (1997). Sales Process Engineering: A Personal Workshop. Milwaukee, Wisconsin: ASQ Quality Press. p. 237. 5. Professional statisticians have welcomed Taguchi's concerns and emphasis on understanding variation: 6. Fisher labeled loss functions as being better suited for American businessmen and Soviet comisars than for empirical scientists (in Fisher's 1956 attack on Wald in the 1956 JRSS). 7. Logothetis, N. and Wynn, H. P. (1989). Quality Through Design: Experimental Design, Off line quality control, and Taguchi's Contributions. Oxford University Press, Oxford Science Publications. pp. 464. 8. Wu, C. F. Jeff and Hamada, Michael (2002). Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley. 9. Box, G. E. P. and Draper, Norman. (2007), Response Surfaces, Mixtures, and Ridge Analyses, Second Edition [of Empirical Model-Building and Response Surfaces, 1987], Wiley. 10. Atkinson, A. C. et al., (2007). Optimum Experimental Designs with SAS, Oxford University Press pp. 511+xvi. 11. http://www.juran.com. 12. S.H. Park, Robust Design and Analysis for Quality Engineering, Chapman & Hall, London, 1996. 13. R. Unal, E.B. Dean, Taguchi approach to design optimization for quality and cost: an overview, in: Proceedings of the International Society of Parametric Analyst 13th Annual, May 21–24, 1991. 14. M.S. Phadke, Quality Engineering Using Robust Design, Prentice- Hall, Englewood Cliffs, NJ, 1989. 15. T. Bendell, Taguchi methods, in: Proceedings of the 1988 European Conference on Taguchi Method, Elsevier, Amsterdam, 13–14 July, 1988. 16. V.K. Roy, Nutek, Inc. http://www.vkroy.com/up-doe.html. 17. W.H. Yang, Y.S. Tarng, Design optimisation of cutting parameters for turning operations based on the Taguchi method, J. Mater. Process Technology 84 (1998) 122–129. 18. T.R. Lin, Experimental design and performance analysis of tin-coated carbide tool in face milling stainless steel, J. Mater. Process Technology, 5654 (2002) 1–7. 19. K. L. Tsui, Modeling and analysis of dynamic robust design experiments, IEE Trans. 31 (1999) 113–1122. 20. C. Zhang, H. P. Wang, “Robust design of assembly and machining tolerance allocations”, IEE Trans. 30 (1998) 17–29 21. C.T. Si, L.I. Tong, Multi response robust design by principal component analysis, Total Qual. Manage. 8 (1997) 409–416. 22. J. Kopac, M. Bahor, M. Sokovic, Optimal machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel workpieces, Int. J. Mach. Tool Manuf. 42 (2002) 707–796. 23. P.G. Benardos, G.C. Vosniakos, Prediction of surface roughness in cnc face milling using neural networks and Taguchi’s design of experiments, Robot. Comput. Integr. Manuf. 18 (2002) 343–351. 24. P.K. Philip, Built-up edge phenomena in machining steel with carbide, Int. J. Mach. Tool Des. Res. 11 (1971) 121–132.
  • 12. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME 20 25. H.Z. Li, X.P. Li, Milling force prediction using a dynamic shear length model, Int. J. Mach. Tools Manuf. 42 (2002) 277–286. 26. M. E. Martellotti, An analysis of the milling process, Trans. ASME (1941) 677–695. 27. E. M. Trent, Metal Cutting, 3rd ed., Butterworths, Heinemann, 1991. 28. C. H. Borneman, Chip thickness in milling, Am. Mach. Ref. Book Sheet 82 (1938) 189–190. 29. S. Madhava Reddy, “Optimization of Surface Roughness in High-Speed End Milling Operation using Taguchi’s Method”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 4, 2013, pp. 249 - 258, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 30. Prabhat Kumar Sinha, Manas Tiwari, Piyush Pandey and Vijay Kumar, “Optimization of Input Parameters of CNC Turning Operation for the Given Component using Taguchi Approach”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 4, 2013, pp. 188 - 196, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.