Experimental Investigation of Material Removal Rate in CNC TC Using Taguchi Approach

Transcription

1 February 05, Volume, Issue JETIR (ISSN-49-56) Experimental Investigation of Material Removal Rate in CNC TC Using Taguchi Approach Mihir Thakorbhai Patel Lecturer, Mechanical Engineering Department, B. & B. Institute of Technology, Vallabh Vidyanagar, India. Abstract This paper deals with the optimization of machining parameters in rough turning of E 50 B0 of standard IS: 06 using carbide insert. During the experiments, controllable parameters like speed, feed and depth of cut are used to explore the effect on material removal rate. The optimization of material removal rate is done using nine experimental runs base on L9 orthogonal array by using Taguchi method. After conducting the experiments the material removal rate was measured and Signal to Noise ratio was calculated. With the help of graphs of raw and SN ratio data, optimum parameter values were obtained and the confirmation experiments were carried out. These results were compared with the results of full factorial method. ANOVA analysis also performed to determine which parameters are the most significant effect on material removal rate. Index Terms ANOVA, Design of Experiments, Material Removal Rate, Orthogonal Array, SN Ratio, Taguchi Method.. Introduction The recent developments in science and technology have put tremendous pressure on manufacturing industries. The manufacturing industries are trying to decrease the cutting costs, increase the quality of the machined parts and machine more difficult materials []. Basically, classical parameter design, developed by Fisher, is complex and not easy to use. Especially, a large number of experiments have to be carried out when the number of the process parameters increases[6]. Traditional experimental design methods are very complex, difficult to use and time consuming. Additionally, these methods require a large number of experiments when the number of process parameters and their levels are increases. In order to minimize the number of experiments required, Taguchi experimental design method, a powerful tool for designing high-quality system, was developed by Taguchi []. Taguchi method is efficient method for designing process that operates consistently and optimally over a diversity of conditions [4]. One of the important steps involved in Taguchi s technique is selection of an orthogonal array (OA). An OA is a small set from all possibilities which helps to determine least no. of experiments, which will further help to conduct experiments to determine the optimum level for each process parameters and establish the relative importance of individual process parameters [5].. Literature Review Mihir Patel et. al. [7],[8] have taken input parameters (controllable factors): speed, feed and depth of cut and only few researcher taken input parameter: nose radius, environment and output parameters: Surface roughness for turning, few researcher taken output parameter: material removal rate, They also found that for surface roughness the most significant parameters are speed, feed and nose radius and least significant parameter is DOC and for MRR the most significant parameters are DOC, feed and speed and least significant parameter is nose radius.. Approach for improve quality of product and process There are various methodologies by which a given process can be optimized. There are different methodologies that are used to improve the quality of product and process. Some widely used approaches in product/process development are[0]. Build-Test-Fix One Factor at a time Design of Experiment (DOE) Build-Test-Fix The Build-test-fix is the most basic approach which is rather inaccurate as the process is carried out according to the resources available, instead of trying to optimize it. In this method the process/product is tested and reworked each time till the results are acceptable. One factor at a time The one factor at a time approach is aimed at optimizing the process by running an experiment at one particular condition and repeating the experiment by changing any other one factor till the effect of all factors are recorded and analyzed. Evidently, it is a very time consuming and expensive approach. In this process, interactions between factors are not taken in to account. Design of Experiment JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 0

2 February 05, Volume, Issue JETIR (ISSN-49-56) The Design of Experiments is referred as one of the most comprehensive approach in product/process developments. It is a statistical approach that attempts to provide a predictive knowledge of a complex, multi-variable process with trials. Following are the major approaches to DOE: Full Factorial Design Taguchi Method Full Factorial Design A full factorial design contains all possible arrangements of a set of factors. This is the most fool proof design approach, but it is also the most costly and time consuming in experimental resources. A common experimental design is the one with all input factors set at two levels each. If there are k factors each at levels; a full factorial design has k runs. Thus for 6 factors at two levels it would take 6 =64 trial runs. Full factorial designs are the most traditional of all design types. There is little scope for uncertainty when you are willing to try all combinations of the factor settings. Unfortunately, the sample size grows exponentially in the number of factors, so full factorial designs are too costly to run for most practical purposes. Taguchi Method Taguchi thus, recommends the use of the loss function to measure the performance characteristics that are deviating from the desired target value. The value of this loss function is further transformed into signal-to-noise (S/N) ratio. This loss function value is further converted into a signal-to-noise (S/N) ratio. Basically, the performance characteristic has following three categories of the S/N ratio [],[9]. The Nominal-the-Better characteristics S/N ratio = 0 log 0 [Square of mean/variance] () The Larger-the-Better characteristics S ratio = 0 log N 0 [mean of sum squares of reciprocal of measured data] () The Lower-the-Better characteristics S/N ratio = 0 log 0 [mean of sum of square of measured data] () Steps involved in Taguchi Method: Determine the Quality/Performance Characteristic to be Optimized. Identify the testing condition and determine the number of levels and possible intereaction between the process Select appropriate Orthogonal Array and assign OA to process parameters. Conduct the experimental according to OA matrix Analysed the results. Select the optimal level of process parameters. Verify the optimal process parameters through conformation experiments. 4. Taguchi Experimental Procedure Taguchi is systemic approach to achieve optimal level of process parameters for given performance characteristics. Here material removal rate is taken as performance characteristics for given case study and experiments are carried out on CNC TC for rough turning. The procedure is given below. 4. Determine the Quality/Performance characteristic to be optimized For a given case study the quality/performance characteristics is material removal rate. The material removal rate has been calculated from the difference of weight of work before and after machining by using following formula. Wi-W f MRR= ρt Where, Wi = weight of work before machining Wf = weight of work after machining ρ = density of E50 B0 grade material t = machining time in sec For given case study the objective function taken larger the better and JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 04

3 February 05, Volume, Issue JETIR (ISSN-49-56) n S/N ratio for given function = -0 log 0 n y i i 4. Identify the testing condition and ddetermine the number of levels and possible interaction between the process The experimental setup and testing condition as below: Work piece material : E 50 B0 of standard IS: 06 Machine Tool : Sprint 0 TC made by Batliboi The chemical composition of : E 50 B0 material: Table Chemical composition of E 50 B0 of standard IS: 06[] Grade Quality C % Mn % S % P % Si C. E. % Max. Max. Max. Max. Max. Max. E 50 B Cutting Tool : Sandvik CNMG PR 45 Testing Equipment: Digital weight scale and stop watch The controllable factors and their levels were decided for conducting the experiment, based on a brain storming that was held with a group of people and also considering the guide lines given in the operator s manual provided by the manufacturer of the lathe machine and tool inserts company. The factors and their levels are shown in table Parameters/ Factors Table Cutting Parameters and their levels Levels Speed (rpm) Feed (mm/rev) Depth of cut (mm) Select appropriate Orthogonal Array and assign OA to process parameters The selection of orthogonal array based on number of factors to be studied, number of levels for each factor and Number of interactions to be estimated. For above mentioned parameters/factors and their levels for single interaction Degree of freedom (DOF) for Speed = (-) = Degree of freedom (DOF) for Feed = (-) = Degree of freedom (DOF) for Depth of cut = (-) = The total degree of freedom = + + = 6 Therefore Minimum number of experiment = Total DOF for parameters + = 6 + Minimum number of experiment = 7 L 9 orthogonal array of Taguchi is selected. L 9 orthogonal array designed as shown in below table. Table L 9 Orthogonal Array Design in rpm in mm/rev in mm JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 05

4 February 05, Volume, Issue JETIR (ISSN-49-56) 4.4 Conduct the experimental according to OA matrix According to above OA, experiments were conducted with their factors and their levels as mentioned in table. The experimental matrix with the selected values of the factors is shown in Table. Each of the above 9 experiments were conducted times to account for the variations that may occur due to the noise factors. The material removal rate (MRR) was measured using the Digital scale weight and stop watch. The table 4 shows the values of material removal rate obtained from different experiments with their SN ratio value. Exp. No. Table 4 Experimental Result of Material Removal Rate for Rough Turning in rpm in mm/rev in mm MRR In mm /sec SN Ratio for MRR Analysed the result Figures show that there is significance interaction between the processes parameters in affecting the material removal rate, since the responses at different levels of process parameters for a given level of parameter value are almost interact with each other. Interaction Plot for MRR Data Means Figure Interaction Plot for MRR 4.6 Select the optimal level of process parameters The average values of material removal rate (MRR) for rough turning for each process parameter at levels (, & ) for raw data and S/N ratio data are plotted in Figures and respectively. Figures & told that the material removal rate maximum at for speed at level, feed at level and depth of cut at level. Then, optimal sequence for the material removal rate for rough turning is A B C. JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 06

5 February 05, Volume, Issue JETIR (ISSN-49-56) Main Effects Plot for Means for MRR Data Means Mean of Means Figure Main effects plot for means for MRR Main Effects Plot for SN ratios for MRR Data Means Mean of SN ratios Signal-to-noise: Larger is better Figure Main Effects Plot for SN Ratio for MRR 4.7 Verify the optimal process parameters through conformation experiments Estimation of means The optimal setting of selected process parameters is: A B C The estimated mean of the response characteristic Ra (Turning) can be computed as: μ A4 B C D = T + (A T ) + (B T ) + (C T ) = A + B + C + (T ) = (58.7) = 6.9 mm /sec JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 07

6 February 05, Volume, Issue JETIR (ISSN-49-56) The 95 % confidence interval of the predicted optimum means is: = (μ A B C CI) < μ A B C < (μ A B C + CI) = 6.48 < μ A B C < 0.06 Four confirmation experiments were thus conducted at the optimal settings for turning process parameters recommended by the investigation. The average value of material removal rate (Rough Turning) while turning E 50 B0 of standard IS: 06 material with CVD -coated carbide inserts was found to 55.8 mm /sec. This result was within the 95% confidence interval of the predicted optimal value of the selected responding characteristic Ra (Turning). Hence the optimal settings of the process parameters, as predicted in the analysis, can be implemented. 5. Full Factorial Design A full factorial design contains all possible arrangements of a set of factors. As far as the following experiments are concerned the factors i.e.; speed, feed, and depth of cut were considered at different levels as shown in Table. These were compared with the results of the fractional factorial that was conducted using Taguchi method. The full factorial design matrix as shown in below. Exp. No. Table 5 Full Factorial Design Matrix in rpm in mm/rev in mm MRR In mm /sec Comparisons of Full factorial design with Taguchi parameter design JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 08

7 February 05, Volume, Issue JETIR (ISSN-49-56) From above full factorial matrix the maximum material removal rate ware obtained at 00 rpm, 0. mm/rev and.5 mm of depth of cut. From Taguchi parameter design the optimum parameter levels obtained were also the same. Thus, it can be noted that Taguchi parameter design will also give same and accurate results with lesser number of experiments. 7. ANOVA Analysis of variance is important analysis to find out which parameters are most significant effect on the performance characteristics and also help to determine to % contribution of each parameters. In the analysis, the sum of squares and variance are calculated. F- test value at 95 % confidence level is used to decide the significant factors affecting the process and percentage contribution is calculated [9]. The ANOVA analysis for percentage calibration is shown in Table 6. It is clear from the table that depth of cut, feed and speed are the most significant factors for material removal rate and their percentage contribution 67.6, 0.5 and 6. respectively. Table 6 Analysis of Variance for MRR Source DF Seq SS Adj SS Adj MS F P % Contribution Error Total Conclusion The present study was carried out to study the effect of input parameters on the material removal rate. The following conclusions have been drawn from the study: The parameters considered in the experiments are optimized to attain maximum material removal rate. The best combination setting of controllable process parameters for rough turning within the selected range is as follows: Speed i.e. 00 rpm (A ). Feed rate i.e. 0. mm/rev (B ). Depth of cut should be.5 mm (C ). From ANOVA analysis, Depth of cut, feed and speed are the most significant factors for material removal rate and its percentage contribution are 67.6, 0.5 and 6. respectively. The Taguchi s robust orthogonal array design is suitable to analyze the material removal rate problem as described in this paper. Taguchi parameter design can be show the same result as full factorial design experiments. Taguchi s approach can be analyzed any kind of problem. REFERENCE []. Ashish Bhateja, Jyoti Bhardwaj, Maninder Singh & Sandeep Kumar Pal, Optimization of Different Performance Parameters i.e. Surface Roughness, Tool Wear Rate & Material Removal Rate with the Selection of Various Process Parameters Such as Speed Rate, Feed Rate, Specimen Wear, Depth Of Cut in CNC Turning of EN4 Alloy Steel An Empirical Approach, The International Journal of Engineering And Science, Vol., Issue, 0, pp. 0-. []. BIS, Hot Rolled Medium And High Tensile Structural Steel Specification, 7 th Edn; Bureau of Indian Standard, New Delhi, 0 []. Ilhan Asilturk, & Harun Akkus, Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method, Elsevier Journal, Measurement, 44, 0, pp [4]. Jitendra Verma, Pankaj Agrawal & Lokesh Bajpai, " Turning Parameter Optimization For Surface Roughness Of ASTM A4 Type- Alloys Steel By Taguchi Method", International Journal of Advances in Engineering & Technology, Vol., Issue, March 0, pp [5]. M. Kaladhar, K. Venkata Subbaiah, & Ch. Srinivasa Rao, Determination of Optimum Process Parameters during turning of AISI 04 Austenitic Stainless Steels using Taguchi method and ANOVA, International Journal of Lean Thinking, Volume, Issue, June 0, pp. -9. [6]. M. Nalbant, H. Gokkaya & G. Sur, Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Elsevier Journal, Materials and Design, 8, 007, pp [7]. Mihir Patel & Vivek Deshpande, Investigation of Effect of Process Parameters on Different Performance Parameters for Aluminum Alloy on CNC Review, Volume Issue, February 05, pp. JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 09

8 February 05, Volume, Issue JETIR (ISSN-49-56) [8]. Mihir Patel & Vivek Deshpande, Optimization of Machining Parameters for Turning Different Alloy Steel Using CNC Review, International Journal of Innovative Research in Science, Engineering and Technology, Vol., Issue, February 04, pp [9]. Ross Philip J, Taguchi Techniques for Quality Engineering; nd Edn; Tata McGraw -Hill Publication Limited, New Delhi, 005. [0]. Srinivas Athreya and Dr Y.D.Venkatesh, Application Of Taguchi Method For Optimization Of Process Parameters In Improving The Surface Roughness Of Lathe Facing Operation International Refereed Journal of Engineering and Science, Volume, Issue, Nov.0, pp.-9. []. Upinder Kumar Yadav, Deepak Narang & Pankaj Sharma Attri, Experimental Investigation And Optimization Of Machining Parameters For Surface Roughness In CNC Turning By Taguchi Method, International Journal of Engineering Research and Applications, Vol., Issue 4, July-August 0, pp JETIR50008 Journal of Emerging Technologies and Innovative Research (JETIR) 0