and other methods. The key element for achieving high quality at low cost is Design of Experiments (DOE). Response surface methodology (RSM) is an effective ...
International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014)
Optimization of Process Parameters in Turning Operation Using Response Surface Methodology: A Review Ranganath M S1, Vipin2, Harshit3 1
1, 2
Associate Professor, 2Professor, 3Student Production and Industrial Engineering, 3Mechanical Engineering, Delhi Technological University, Delhi, India This causes high manufacturing cost and low product quality .Surface roughness is mainly a result of process parameters such as tool geometry (i.e. nose radius, edge geometry, rake angle) and cutting conditions (feed rate, cutting speed, depth of cut) [8].Surface roughness is harder to attain and track than physical dimensions are, because relatively many factors affect surface roughness. Some of these factors can be controlled and some cannot. Controllable process parameters include feed, cutting speed, tool geometry, and tool setup. Other factors, such as tool, work piece and machine vibration, tool wear and degradation, and work piece and tool material variability cannot be controlled as easily [13]. The important cutting parameters considered for discussion here are cutting speed, feed and depth of cut. It is found in most of the cases surface roughness decreases with increase in cutting speed and decrease in feed and depth of cut. Since these cutting parameters will decide about the type of chips which we expect at the time of machining of a single constant material thus we have to analyze them for no such built-up edge chips formation.
Abstract— The parameters affecting the roughness of surfaces produced in the turning process for various materials have been studied by many researchers. Design of experiments were conducted for the analysis of the influence of the turning parameters such as cutting speed, feed rate and depth of cut on the surface roughness. The results of the machining experiments were used to characterize the main factors affecting surface roughness by the DOE techniques like Taguchi, Full factorial, Response Surface Methodology and other methods. The key element for achieving high quality at low cost is Design of Experiments (DOE). Response surface methodology (RSM) is an effective tool for robust design, it offers a simple and systematic qualitative optimal design to a relatively low cost. In this paper RSM, an approach of DOE used by various researchers to analyze the effect of process parameters like cutting speed, feed, and depth of cut on Surface Roughness of work material while machining with various tools and to obtain an optimal setting of these parameters that may result in good surface finish have been discussed. Keywords—DOE, Response surface methodology, ANOVA, Surface Roughness, Turning.
Response Surface Methodology RSM is a collection of mathematical and statistical techniques that are useful for modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response [10].RSM can be defined as a statistical method that uses quantitative data from appropriate experiments to determine and simultaneously solve multi-variable equations correlating the dependent parameters(response) such as cutting force, power consumption, surface roughness, tool life with independent parameters (input variables). cutting speed, feed rate and depth of cut, in a turning process. The graphical representations of these equations are called response surfaces, which can be used to describe the individual and cumulative effect of the input variables on the response and to determine the mutual interactions between the input variables and their subsequent effect on the response.
I. INTRODUCTION The need for selecting and implementing optimal machining conditions and most suitable cutting tools has been felt very important over few decades.Surface roughness has become the most significant technical requirement and it is an index of product quality. In order to develop a surface roughness model and optimize, it is essential to understand the current status of work in this area. A good surface finish is desired in order to improve the properties, fatigue strength, corrosion resistance and aesthetic appeal of the product. The manufacturing industries specially are focusing their attention on dimensional accuracy and surface finish. In order to obtain optimal cutting parameters to achieve the best possible surface finish, manufacturing industries have resorted to the use of handbook based information and operators experience. This traditional practice leads to improper surface finish and decrease in the productivity due to suboptimal use of machining capability.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) The work which initially generated interest in the package of technique was by box and Wilson in 1951.RSM can be used in the following ways [20]: 1) To determine the factor levels that will simultaneously satisfy a set of desired specifications, 2) To determine the optimum combination of factors that yields a desired response and describes the response near the optimum, 3) To determine how a specific response is affected by changes in the level of the factors over the Specified levels of interest, 4) To achieve a quantitative understanding of the system behavior over the region tested, 5) To predict product properties throughout the region, even for a factor combinations not actually run, 6) To find the conditions necessary for process stability (insensitive spot). To generate contour surface [5] Response surface methodology, or RSM, is a collection of mathematical and statistical techniques in which a response of interest is influenced by several variables and the objective is to optimize this response. For example, suppose that a chemical engineer wishes to find the levels of temperature (x1) and pressure (x2) that maximize the yield (y) of a process. The process yield is a function of the levels of temperature and pressure, y = f (x1, x2) +e Where e represents the noise or error observed in the response y. Then the surface represented by h= f (x1, x2), which is called a Response surface. We usually represent the response surface graphically, where h is plotted versus the levels of x1 and x2. To help visualize the shape of a response surface, we often plot the contours of the response surface as well. In the contour plot, lines of constant response are drawn in the x1, x2 planes. Each contour corresponds to a particular height of the response surface. Objective is to optimize the response. In RSM, polynomial equations, which explain the relations between input variables and response variables, are constructed from experiments or simulations and the equations are used to find optimal conditions of input variables in order to improve response variables. For the design of RSM, many researchers have used central composite design (CCD) for their experiments. CCD is widely used for fitting a secondorder response surface. CCD consists of cube point runs, plus center point runs, and plus axial point runs.
Figure 1.Steps in Response Surface Methodology (RSM).
In design optimization using RSM, the first task is to determine the optimization model, such as the identification of the interested system measures and the selection of the factors that influence the system measures significantly. To do this, an understanding of the physical meaning of the problem and some experience are both useful. After this, the important issues are the design of experiments and how to improve the fitting accuracy of the response surface models. DOE techniques are employed before, during, and after the regression analysis to evaluate the accuracy of the model.RSM also quantifies relationships among one or more measured responses and the vital input factors.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) Depending on the capacity of the machine, an augment length of ±1 was chosen. The augment points consist of three levels for each of the independent variables denoted by -1, 0, 1. These six experiments were repeated twice to develop the second-order model. The resulting 12 or 24 experiments form the central composite design with 23 designs. Regression analysis was successfully used to develop the surface roughness model. The surface roughness equation showed that the feed is the main influencing factor on the surface roughness, followed by cutting speed and depth of cut in the operation model. Increasing any of these three cutting variables increases the surface roughness. Dual-response contours provided useful information about the maximum attainable surface roughness for a given metal removal rate as a function of all three independent cutting variables.
32 Fig2: To generate contour surface
The three factors speed, feed rate, depth of cut, selected in the screening experiment, will be used in CCD. The process can be studied with a standard RSM design called a Central composite design (CCD). The factorial portion is a full factorial design with all factors at three levels, the star points are at the face of the cube portion on the design which correspond to value of -1. This is commonly referred to as a face centered CCD. The center points, as implied by the name, are points with all levels set to coded level 0, the midpoint of each factor range, and this is repeated six times. Twenty experiments to be performed. For each experimental trial, a new cutting edge to be used. The latest version of the Minitab or Design Expert may be used to develop the experimental plan for response surface methodology. The same software can also be used to analyze the data collected.
Fig3: Representation of a CCD
Alexandru STANIMIR.et.al.,[22] have presented a paper where in order to find out a mathematical relation of polynomial second degree type which describe, in finish turning of hardened 205 Cr115 steel, the roughness parameter Ra dependence on cutting edge wear, depth of cut, feed rate and cutting speed, a factorial design methodology was used. The experimental tests were done according to a composed, central, four-factor five-level factorial program. The established second degree polynomial relationship for the Ra calculus as a function of the cutting conditions and the flank wear approximate in a satisfactory way the studied phenomena. The main influence on the surface roughness was exerted by the feed rate and flank wear.The interactions of some parameters as feed rate and flank wear had a great influence on the roughness values of the machined surface. Karin Kandananond [7] used Design of Experiment for the determination of Empirical Model for Surface Roughness in Turning Process.
II. LITERATURE REVIEW Vipin and Harish Kumar [24] used Response surface methodology for their investigations. The surface roughness models were developed for turning leaded gun metal under dry conditions. The models were developed in terms of cutting speed, feed rate and depth of cut obtained experimentally. The effects of cutting variables (cutting speed, feed and depth of cut) on surface roughness had been investigated by Central composite Design. The firstorder model was developed by an experimental design consisting of 12 experiments. Twelve experiments constitute Eight experiments (23 factorial designs) and Four experiments (an added centre point repeated four times) as shown in Fig.2. This was done to predict the ‗b‘ parameters as used in the Equation. The blocks provide the confidence interval of the parameters and help in the analysis of variance. A second-order model was developed by adding six augment points to the factorial design.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) This process was performed in the final assembly department at a manufacturing company which supplies fluid dynamic bearing (FDB) spindle motors for hard disk drives (HDDs). The work pieces used were the sleeves of FDB motors made of ferritic stainless steel, grade AISI 12L14. A 2k factorial experiment was used to characterize the effects of machining factors, depth of cut, spindle speed and feed rate on the surface roughness of the sleeve. The results show that the surface roughness was minimized when the spindle speed and feed rate were set to the highest levels while the depth of cut was set to the lowest level. Even though the results from this research were process specific, the methodology deployed can be readily applied to different turning processes. As a result, practitioners have guidelines for achieving the highest possible performance potential. The purpose of this research is to quantify the effect of depth of cut, spindle speed and feed rate on surface roughness of the FDB sleeve in HDD. The factorial design was utilized to obtain the best cutting condition which leads to the minimization of the surface roughness. The half normal plot and ANOVA indicate that the feed rate (C) is the most significant factor followed by spindle speed (B) and feed rate (A). Moreover, it was interesting to note that there are interactions among these three factors with the highest order term, ABC. Regarding the model validation, the regression model developed proves accuracy and has the capability to predict the value of response within the limits of factors investigated. After the optimal cutting condition is implemented, the surface roughness was significantly reduced about 8 percent. In addition to the factorial design experiment, the RSM and Taguchi design were proved to be potential methodologies to develop an empirical model and optimize the surface roughness of the metal work pieces. M. Aruna and V. Dhanalaksmi [3] used Inconel 718, a nickel based super-alloy accounting for about 50% by weight of materials used in an aerospace engine, mainly in the gas turbine compartment. This is owing to their outstanding strength and oxidation resistance at elevated temperatures in excess of 5500 C. machiningis a requisite operation in the aircraft industries for the manufacture of the components especially for gas turbines. This paper is concerned with optimization of the surface roughness when turning Inconel 718 with cermet inserts. Optimization of turning operation is very useful to reduce cost and time for machining. The approach was based on Response Surface Method (RSM). In their work, second-order quadratic models were developed for surface roughness, considering the cutting speed, feed rate and depth of cut as the cutting parameters, using central composite design.
The developed models were used to determine the optimum machining parameters. These optimized machining parameters were validated experimentally, and it was observed that the response values were in reasonable agreement with the predicted values. The experiment was performed by using a PMT –TNS-25 CNC lathe. Inconel 718 cylindrical rod is considered as the work piece material. Titanium carbide based cermet inserts (Triangular) were used as the cutting tool. The consequences of the experimental data is used for predicting the outcome of assorted input machining parameters such as cutting speed, feed and depth of cut on the surface roughness when machining Inconel 718 using cermet inserts. A non-linear regression equation was developed and projected. Cutting speed has the strongest effect on the surface roughness among the selected parameters; it was inversely proportional to the response. It was found that the surface roughness could be controlled in the design stage which was the most effective and inexpensive way. Y. Sahin, A.R. Motorcu [17] presented a paper in which the surface roughness model was developed in terms of main cutting parameters such as cutting speed, feed rate and depth of cut, using response surface methodology. Machining tests were carried out in turning AISI 1050 hardened steels by cubic boron nitride (CBN) cutting tools under different conditions. The model predicting equations for surface roughness of Ra, Rz and Rmax were developed using an experimental data when machining steels. The results indicate that the feed rate was found out to be dominant factor on the surface roughness, but it decreased with decreasing cutting speed, feed rate, and depth of cut for these tools. In addition, average surface finish of Ra value produced by CBN cutting tool was about 0.823 µm when machining hardened steels. However, the Ra value decreased about 0.55 µm in terms of trial conditions. Moreover, a good agreement between the predicted and experimental surface roughness was observed within reasonable limit. To develop a second-order model, a design consisting of 18 experiments was conducted. Eight experiments constitute 23 factorial designs with an added center point repeated four times, the added center point being used to estimate pure error. An augment length of 2 was chosen depending on the capacity of the center lathe. The augments point consists of three levels for each of the independent variables denoted by -2,-1, 0, +1, +2. The following conclusions were drawn from the cutting conditions in machining hardened AISI 1050 steels using CBN/TiC cutting tools.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) The result clearly showed that feed rate was the most powerful factor on surface roughness for all tools. In general, surface roughness of Ra, Rz and Rmax parameters decreased with decreasing main cutting parameters of V (cutting speed), f (feed rate), d (depth of cut) for CBN cutting tools. Furthermore, the average surface finish values of Ra produced by CBN/TiC cutting tools was about 0.823 µm for machining the hardened carbon steel. However, this Ra value even reduced about 0.55 µm depending upon cutting parameters. Moreover, the regression model showed that the predicted values had been very close to experimental values of surface roughness when machining hardened steels. M. Naga PhaniSastry and. K. Devaki Devi [17] have presented a paper which was concerned with the effect of turning process parameters (cutting speed, feed rate, and depth of cut) on the metal removal rate and surface roughness as responses or output parameters. Response surface methodology (R.S.M), determine and present the cause and effect of the relationship between true mean response and input control variables influencing the response as a two or three dimensional surface. R.S.M had been used for designing a three factor with three level central composite factors design in order to construct statistical models capable of accurate prediction of responses. The results obtained shows that the application of R.S.M can predict the effect of machining parameters on MRR and surface roughness. A case of straight CNC turning of aluminum bar using HSS tool was being considered. The predicted optimal setting ensured minimization of surface roughness and maximization of MRR (Material Removal Rate). Optimal result was verified through confirmatory test. The roughness is measured by surface roughness meter (Stylus Type) and the force is measured by dynamometer .The results were then recorded to analyze the effect of different parameters and their levels on surface roughness and MRR. The model for three different parameters namely speed, feed and depth of cut for turning process in CNC LATHE on aluminium material were developed using response surface method(RSM).The second order models had been validated with the analysis of variance. It was found that the feed and depth of cut had more significant effect while speed had less significant effect on MRR and feed and depth of cut had equal significance on surface roughness (Ra).
LB Abhang and M Hameedullah [1] have published a paper in which experimental investigations had been conducted to determine the effect of the tool geometry (effective tool nose radius) and metal cutting conditions (cutting speed, feed rate and depth of cut) on surface finish during the turning of EN-31 steel. First and second order mathematical models were developed in terms of machining parameters by using the response surface methodology on the basis of the experimental results. The surface roughness prediction model had been optimized to obtain the surface roughness values by using LINGO solver programs. LINGO is a mathematical modeling language which is used in linear and nonlinear optimization to formulate large problems concisely, solve them, and analyze the solution in engineering sciences, operation research. It gives minimum values of surface roughness and their respective optimal conditions. The factorial design with eight added centre points (24 + 8) used in this work is a composite design. A reliable surface roughness model for steel turning was developed using RSM and incorporated cutting speed, feed rate, depth of cut, and the tool nose radius. The study was optimized by the LINGO-solver approach, which is a global optimization technique. This has resulted in a fairly useful method of obtaining process parameters in order to attain the required surface quality. The optimal parameter combination of the turning process corresponded to a cutting speed of 189 m/minute, a feed rate of 0.06 mm/revolution, a depth of cut of 0.2 mm, and a tool nose radius of 1.2 mm by the Lingo-solver approach. This had validated the trends available in the literature and extended the data range to the present operating conditions, apart from improving the accuracy and modeling by involving the most recent modeling method. The application of LINGO-solver optimization to obtain optimal machining conditions will be quite useful at the computational planning stage in the production of high quality goods with tight tolerances by a variety of machining operations, and in the adaptive control of automated machine tools. Srinivasan, A. et al [21] published a paper in which an attempt has been made to model the machinability evaluation through the response surface methodology in machining of homogenized 10% micron Al2O3 LM25 Al MMC manufactured through stir casting method. Results:
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) The combined effects of three machining parameters including cutting speed (s), feed rate (f) and depth of cut (d) on the basis of three performance characteristics of tool wear (VB), surface Roughness (Ra) and cutting Force (Fz) were investigated. The contour plots were generated to study the effect of process parameters as well as their interactions. The process parameters were optimized using desirability-based approach response surface methodology. RSM model have been developed for predicting tool life, surface roughness and cutting force. The optimized cutting condition that gives lower surface finish and cutting force when machining micron MMCs had been identified: Cutting speed ‗V‘ 100 m/min, Feed ‗f‘ 0.1 mm/rev and Depth of cut ‗a‘ 0.10 mm. The surface roughness improves with increase of the cutting speed whilst increasing feed adversely affects the surface roughness. The tool wear increases with increase of the cutting speed, the feed and the depth of cut. Among the machining parameters cutting speed had the most dominant effect on tool wear. The cutting force almost linearly varies with feed. At low cutting speed the cutting force was higher and the interactions of cutting speed with feed and depth of cut with feed dominantly affects the cutting forces. The machining parameters for turning process were optimized using Taguchi‘s technique for minimizing the tool wear, surface roughness and cutting force. The developed model had been validated experimentally and exhibit low values of error. Ranganath. M. S. et al. [16], presented the findings of an experimental investigation into the effects of speed, feed rate and depth of cut on surface roughness in CNC turning of Aluminium (KS 1275).
Response surface methodology and Taguchi techniques were used to accomplish the objective of the experimental study. L27 orthogonal array design was used for conducting the experiments. RSM as well as Taguchi‘s techniques revealed that Feed is the most significant factor in minimizing surface roughness followed by speed and depth of cut. RSM technique predicted results better than the Taguchi‘s technique. Contour Plot of Ra vs C, B 1.0 0.50 0.75 1.00 1.25 1.50 1.75
C
0.5
0.0
Ra < – – – – – – >
0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.00
Hold Values A 0
-0.5
-1.0 -1.0
-0.5
0.0 B
0.5
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Figure 4: Contour Plot of Ra vs C, B [16]
Table 1: Design Table [16] Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Blk 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
A 0 0 -1 0 0 -1 -1 0 -1 1 0 1 -1 0 0 1 0 1 1 0
B 0 0 1 0 1 -1 -1 0 1 1 0 1 0 0 -1 -1 0 -1 0 0
C 0 -1 1 0 0 1 -1 0 -1 1 1 -1 0 0 0 -1 0 1 0 0
Figure 5: Optimization Plot [16]
Young Kug Hwang and Choon Man Lee [6] paper presents an investigation into the MQL (minimum quantity lubrication) and wet turning processes of AISI 1045 work material with the objective of suggesting the experimental model in order to predict the cutting force and surface roughness, to select the optimal cutting parameters, and to analyze the effects of cutting parameters on machinability. Fractional factorial design and central composite design were used for the experiment plan.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) Cutting force and surface roughness according to cutting parameters were measured through the external cylindrical turning based on the experiment plan. The measured data were analyzed by regression analysis and verification experiments were conducted to confirm the results. From the experimental results and regression analysis, this research project suggested the experimental equations, proposed the optimal cutting parameters, and analyzed the effects of cutting parameters on surface roughness and cutting force in the MQL and wet turning processes., a number of experiments should be done to confirm results. A two-level fractional factorial design with resolution V, was used for screening experiments. The reason for the usage of resolution V was to analyze the main effect and two-factor interactions. Generally, in resolution V,the main effects are confounded with four-factor interactions, and two-factor interactions are confounded with three-factor interactions. If interactions higher than three-factor interactions can be ignored, the main effect and two-factor interactions can be analyzed. In this paper, in order to select the optimal cutting parameters and to analyze the effect of cutting parameters on surface roughness and cutting force, experimental equations were suggested. Within the range of cutting parameters used in this study, we were able to draw the following conclusions. Using fractional factorial design, nozzle diameter, cutting speed, feed rate, and depth of cut were found as the parameters affecting the surface roughness of the MQL turning. Experimental equations, which can estimate the surface roughness and cutting force in the MQL and wet turning processes of AISI 1045 work material, were suggested with RSM. From the verification experiment, the cutting force equations confirmed the validity in the MQL and wet turning processes, but the surface roughness equations were not appropriate for accurate prediction. When considering surface roughness and cutting force at the same time in the MQL turning process, the optimal combination of cutting parameters to maximize the machinability was as follows: nozzle diameter of 6mm, cutting velocity of 361m/min, feed rate of 0.01mm/rev, and depth of cut of 0.1mm. In the case of the wet turning process, the optimal combination of cutting parameters consisted of nozzle diameter of 6mm, cutting speed of 394m/min, feed rate of 0.02mm/rev, and depth of cut of 0.1mm. According to the experiment results, cutting speed and depth of cut showed opposite effects on cutting force and surface roughness. Therefore, cutting conditions should be set under a clear standard because the optimal combination of cutting conditions could be different depending on machinability.
If we consider only surface roughness and cutting force, MQL turning has more advantages than wet turning. Ali riza motorcu [11] uses the response surface methodology to investigate the effects of main cutting parameters such as cutting speed, feed rate, and depth of cut on the surface roughness when turning AISI 1050 carbon steel. Machining tests were carried out with uncoated ceramic (KY1615) and coated ceramic cutting tools (KY4400). Optimal machining conditions for the desired surface finish were determined. The adequacy of the second order developed model was analyzed by using analysis of variance. The experimental results indicated that the feed rate was the dominant factor, followed by the depth of cut. The cutting speed showed the minimal effect on the surface roughness. It could be seen that the KY1615 tool produced a better surface roughness than the KY4400 tool. It was shown that average surface roughness‘ of Ra values were about 2.515 μm, 2.984 μm for the KY1615, KY4400 cutting tools, respectively. Furthermore, the analysis of variance for the second-order model indicated that squares terms were significant on the roughness, but interaction terms of cutting parameters were insignificant for both cutting tools. To develop a second-order model, a design consisting of 18 experiments was conducted. Details of the model and design are given elsewhere (Sahin and Motorcu, 2004; Motorcu, 2006). 18 experiments constitute 23 factorial designs with an added center point repeated four times, the added center point being used to estimate pure error. An augment length of 2 was chosen depending on the capacity of the center lathe. The augments point consists of three levels for each of the independent variables denoted by -2, -1, 0, +1, +2. The RSM was adopted to investigate the effects of machining factors such as cutting speed, feed rate, and depth of cut on the surface roughness when turning the AISI 1050 carbon steel. The feed rate was the dominant factor, followed by the depth of cut. The cutting speed showed the minimum effect on the surface roughness. Furthermore, it could be seen that the KY1615 cutting tool produced a better surface roughness than that of the KY4400 tool. The average surface roughness, Ra values were about 2.515 μm, 2.984 μm for the KY1615, KY4400 cutting tools, respectively. It was found that it was appropriate when machining the mild steel with ceramic based cutting tools under high speed conditions. Moreover, the ANOVA showed that the quadratic effects were significant on the roughness while the interactions terms of cutting parameters were statistically insignificant for both cutting tools.
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) Alagarsamy S.V, Rajakumar N.[2] used an effective approach for the optimization of turning parameters based on the Taguchi‘s method with Response surface methodology (RSM) .The paper discusses the use of Taguchi‘s technique for minimizing required surface roughness and maximizing the material removal rate in machining Aluminium Alloy 7075 using TNMG 115 100 tungsten carbide tool. Experiments were conducted based on the established Taguchi‘s technique L27orthogonal array and Minitab-16 statistical software was used to generate the array. Three machining parameters were chosen as process parameters: Cutting Speed, Feed rate and Depth of Cut. The orthogonal array, signal to noise ratio and analysis of variance were employed to study the performance characteristics in CNC turning operation. Optimal values of process parameters for desired performance characteristics were obtained by Taguchi design of experiment. A second order mathematical model in terms of cutting parameters was also developed using response surface model. the Taguchi method and Response surface methodology was applied for analysing to get a minimum surface roughness and maximum material removal rate for turning process of Aluminium Alloy 7075 using CNC machine via considering three influencing input parameters- Speed, Feed and Depth of Cut. The optimize results for Surface Roughness indicate that the input parameter feed had most influencing to the quality characteristics of surface roughness. Best parameters found for surface finish machining were-Cutting Speed: 500 m/min; Feed: 0.10 mm/rev; Depth of Cut: 0.3mm. For depth of cut being most affecting parameter .Whenever the MRR increases feed and depth of cut were gradually increases. Finally found the best parameters of MRR wereCutting Speed: 1500 m/min; Feed: 0.20 mm/rev; Depth of Cut: 0.8mm. The equations obtained from Response Surface Methodology for Surface Roughness, MRR were having R-sq. values as 98.85%, and 97.56% respectively. Predicted response values were calculated from Taguchi Analysis and Response Surface Methodology were very similar. Wasif G. M. & Safiulla M. [25] have presented a paper to find the effects of the various machining (turning) process parameters on the machining quality of NICROFER C263 ALLOY and to obtain the optimal sets of process parameters so that the quality of machined parts can be optimized. The working ranges and levels of the machining process (turning) parameters were found using three factors. Cutting speed (V- m/min), feed rate (f – mm/rev) and depth of cut (d - mm).
The Design-Expert software had been used to investigate the effects of the Machining process parameters and subsequently to predict sets of optimal parameters for optimum quality characteristics. The response surface methodology (RSM) in conjunction with second order central composite rotatable design had been used to develop the empirical models for response characteristics. Desirability functions had been used for simultaneous optimization of performance measure. Confirmation experiments was further conducted to validate the results. Conclusion: Quadratic model was fitted for surface roughness. The results show that the surface roughness did not vary much with experimental depth of cut in the range of 0.1 to 0.15 mm. Feed rate was the dominant contributor, accounting for 69.06% of the variation in surface roughness whereas cutting speed accounts for 3.30% and depth of cut 2.41%. Secondary contributions of interaction effect between speed and depth of cut (3.90%), second order (quadratic) effect of cutting speed (17.11%) and feed (4.2 %). The values of feed, speed, depth of cut had been found for best surface finish (lowest surface roughness). These were 0.14 mm/rev .76.10 m/min and 0.102 mm respectively. The value of surface roughness at these setting is predicted as 0.626558 μm. Basha N.Z.,Vivek S.[4] presented a paper that investigates the effect of process parameter in turning operation to predict surface roughness of Aluminium 6061, by optimizing the input parameters such as spindle speed, feed rate and depth of cut by using coated carbide tool. A second order mathematical model was developed using regression technique and optimization was carried out using Box-Behnken of response surface methodology. The application of response surface methodology for optimizing the input parameters such as spindle speed (rpm), feed rate (mm/min) and depth of cut (mm), the output parameter surface roughness can also be optimized for economical production. Therefore, this study attempts the application of response surface methodology to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness using design- expert 8.0 software. The confirmatory test was conducted and found that the percentage of error within ±1.5%. Conclusions obtained from experimental data were used to predict the surface roughness 'Ra' by developing the regression model using DoE. A good surface roughness was obtained at the feed rate (0.04-0.05 mm/min) and spindle speed (17002000 rpm). The change in feed rate (0.04-0.08 mm/min) had a significant effect on surface roughness (0.72-0.66μm) at higher spindle speed (2000rpm).
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International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 10, October 2014) The change in feed rate (0.04-0.08 mm/min) had no significant effect on surface roughness (0.71-0.72μm) at lower spindle speed (1000 rpm). A feed rate less than 0.06 and depth of cut less than 0.70 had been preferred for obtaining good surface finish. Sahoo P. [18] presents an experimental study of roughness characteristics of surface profile generated in CNC turning of AISI 1040 mild steel and optimization of machining parameters based on genetic algorithm. The three level rotatable central composite designs were employed for developing mathematical models for predicting surface roughness parameters. Response surface methodology was applied successfully in analyzing the effect of process parameters on different surface roughness parameters. The second order mathematical models in terms of machining parameters were developed based on experimental results. The experimentation was carried out considering three machining parameters depth of cut, spindle speed and feed rate as independent variables and three different roughness parameters Centre line average roughness, root mean square roughness and mean line peak spacing as response variables. It was seen that the surface roughness parameters decrease with increase in depth of cut and spindle speed but increase with increase in feed rate. The models selected for optimization had been validated with F-test. The adequacy of the models of surface roughness has been established with Analysis of Variance (ANOVA). Genetic Algorithm was used to determine the optimum machining parameters in order to obtain the best possible surface quality. The confirmatory tests conducted with the optimum parameter combinations show good agreement with the predictions. Subramanian M. et al [23] used RSM and GA to Predict Surface Roughness Based on Process Parameters in CNC Turning of AL7075-T6‖ that deals with the study and development of a regression model to predict surface roughness in terms of geometrical parameter, nose radius of cutting tool TNMG insert and machining parameters, cutting speed and cutting feed rate for machining AL7075T6, using Response Surface Methodology (RSM). The surface roughness of machined surface was measured by Mitutoyo Surftest SJ201. The second order mathematical model in terms of machining parameters was developed for predicting surface roughness. The adequacy of the model was checked by employing ANOVA. The direct and interaction effects were graphically plotted which helps to study the significance of these parameters on surface roughness. Also the mathematical model is optimized using Genetic Algorithm to attain minimum surface roughness.
The predicted values of roughness were found to match closely with the observed values. The optimum values of input parameters were identified as nose radius = 1mm, cutting speed = 3500rpm and feed = 0.1mm. III. CONCLUSIONS On the basis of the experimental results and derived analysis by earlier researchers the studies may be concluded as below: The surface roughness is continuously improved with the increase in cutting speed, but increase in feed rate and depth of cut causes a significant deterioration of surface roughness. The results obtained using the Response Surface Methodology provides a systematic, efficient and easy-to-use approach for the cutting process optimization. Significance of interactions and square terms of parameters is more clearly predicted in Response surface methodology. The Response surface methodology shows significance of all possible combinations of interactions and square terms. Response surface methodology technique requires almost double Time for conducting experiments as that needed for Taguchi technique. Response surface methodology technique can model the response in terms of significant parameters, their interactions and square terms which is not provided by other technique. 3D surfaces generated by Response surface methodology can help in visualizing the effect of parameters on response in the entire range specified Optimization plot is a special feature which can be obtained from Response surface methodology. Thus Response surface methodology is a better tool for optimization and can better predict the effect of parameters on response. REFERENCES [1]
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