Application Of Taguchi Method For Optimization Of Knuckle Joint

Transcription

1 Application Of Taguchi Method For Optimization Of Knuckle Joint Ms.Nilesha U. Patil 1, Prof.P.L.Deotale 2, Prof. S.P.Chaphalkar 3 A.M.Kamble 4,Ms.K.M.Dalvi 5 1,2,3,4,5 Mechanical Engg. Department, PC,Polytechnic, Nigdi, Abstract Knuckle joint is one of the most important components of an automobile. These joints are used for different types of connections e.g. tie rods, tension links in some construction work. The purpose of the this paper is to study and calculate the stresses and deformation in Knuckle joint of TATA-709 vehicle and also to optimize the model of same Knuckle joint. Currently, knuckle joint contains excess material which causes increase in weight of vehicle. That excess weight directly affects the mileage and cost. In this research, CATIA V5 has been used for modeling. This paper report on an optimization of knuckle joint by applying FEA and Taguchi method to improve the quality of manufactured goods and engineering development of design for studying variation. Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with a small number of experiments only. The experimental results are then transformed into a signal-tonoise (S: N) ratio. Taguchi recommends the use of the S/N ratio to measure the quality characteristics deviating from the desired values analysis. Regardless of the category of the quality characteristic, a greater S/N ratio corresponds to better quality characteristics. Therefore, the optimal level of the process parameters is the level with the greatest S:N ratio. Furthermore, a statistical analysis of variance (ANOVA) is performed to see which process parameters are statistically significant. With the S/N and ANOVA analyses, the optimal combination of the process parameters can be predicted. Finally, a confirmation experiment is conducted to verify the optimal process parameters obtained from the parameter design. It is also predicted that Taguchi method is a good method for optimization of various machining parameters as it reduces the number of experiments. Keywords Knuckle Joint, CATIA V5, FEA, Taguchi Method, ANOVA, Minitab 17. I. INTRODUCTION A knuckle joint is used to connect two rods whose axes either coincide or intersect and lie in one plane. Knuckle joint is named so because it is free to rotate about the axis of knuckle pin. The common examples of the knuckle joints are: link of a roller chain, tension link in a bridge structure, tie rod joint of roof truss etc. There are mainly three parts of knuckle joint: a fork, an eye and knuckle pin. The knuckle joint is having some wide application such as Tractors, Bicycle chain, cranes, Automobile wipers, robotic joints structural members etc. In this research, Knuckle joint of TATA-709 is used as main component for study. Structural Steel is used to prepare knuckle joint. 3D modeling CAD software is used to develop model of knuckle. Weight can be reduced in the knuckle component optimization. Factor like stiffness, durability and strength must be satisfied with design targets. To save cost and material, mass or weight reduction is an important issue in automobile industry. DOI: /IJRTER CJBQ2 237

2 Fig. 1 Knuckle Joint Fig.2 CAD Model of knuckle joint While developing model in CAD software seven model has been prepared. Design Of Experiment has been applied. II. Design Of Experiment Design of Experiment involves designing a set of experiments, in which all relevant factors are varied systematically. When the results of these experiments are analyzed, they help to identify optimal conditions, the factors that most influence the results, and those that do not, as well as details such as the existence of interactions and synergies between factors. Design of Experiments (DOE) is widely used in research and development, where a large proportion of the resources go towards solving optimization problems. The key to minimizing optimization costs is to conduct as few experiments as possible. DOE requires only a small set of experiments and thus helps to reduce costs. 2.1 Taguchi Method: Taguchi method involves reducing the variation in a process through robust design of experiments. The overall objective of the method is to produce high quality product at low cost to the manufacturer. The experimental design proposed by Taguchi involves using orthogonal arrays to organize the parameters affecting the process and the levels at which they should be varies. Instead of having to test all possible combinations like the factorial design, the Taguchi method tests pairs of combinations. The Taguchi method is best used when there are an intermediate number of variables (3 to 50), few interactions between variables, and when only a few variables contribute significantly. Three design parameters has been identified for Design Of Experiments and FEA. They are Stress, strain and mass for taking deformation as a output and three levels has been taken for the analysis. PARAMETERS LEVELS X = STRESS [N/MM 2 ] Y = STRAIN[N/MM2] Z= MASS [KG] Table 1: Parameter and their All Rights Reserved 238

3 Table 1, shows the chosen design parameters and their levels used in the FE simulations. The high order interactions among the above three factors are assumed negligible and the information on the main effects can be obtained by running 3³ =27 experiments. Exp.No. A B C Experiment No. Parameters Stress N/mm 2 Strain microstrain Mass Kg Table2 : L9 Taguchi Matrix Table 3:Orthogonal array (L9) of Taguchi method Table 2 shows standard L9 Taguchi Matrix for three parameters and three level. However, the appropriate Taguchi orthogonal array for the above three parameters with three levels is L9 to conduct nine simulations, Table 3. The first column represents the number of simulation and the subsequent columns represent the process parameters and the rows represent simulations with the levels of each parameter. 2.2 ANOVA: Calculations of result averages and averages for factor level effects, which only involve simple arithmetic operations produce answers to major questions that were unconfirmed in the preliminary stages of the investigation. However, questions concerning the influence of factors on the variation of thickness in terms of discrete proportion can only be obtained by performing analysis of variance (ANOVA). 2.3 Signal to Noise ratio: TAGUCHI s main idea was to control the noise factors indirectly by examining how they are affected by different settings of the control factors. He suggested analyzing the joint effects of control and noise factors, and for this purpose, proposed a performance criterion called signal-to-noise All Rights Reserved 239

4 Stress N/mm2 Strain N/mm2 International Journal of Recent Trends in Engineering & Research (IJRTER) Mass Kg Deformation mm S/N Ration Table 4 Response table for signal noise ratio The Experiments are planned according to Taguchi method with L9 orthogonal array and response table gave S/N ratio for different combination of design parameter and their levels. 2.4 Main Effects Graphs Different combination graphs have been plotted to observe the main effect of parameters. I. Stress Vs Deformation. II.Strain Vs Deformation: Graph 1Main Effects plot for stress Vs Deformation Graph 2 Main Effects plot for strain Vs Deformation II. Mass Vs Deformation Graph 3 Main Effects plot for Mass Vs All Rights Reserved 240

5 2.5 S/N ratio graphs S/N Ratio: The signal-to-noise (S/N) ratio used to measure responses relative to the nominal or target value under different noise conditions. The response quality characteristic used in this study was smaller-is-better i.e. the smaller deformation is better in experiment. S/N ration for all 9trials are calculated and graphs plotted using Minitab 17.0 Graph 4 Plot of SN ratios Vs. Stress, Strain, Mass For optimum values of the selected parameters, the level that gives the lowest S/N ratio was chosen. Therefore, it can be assured that the optimum levels for the three significant factors, for the most even deformation are stress of mm, strain of 556, mass of 2.204kg. IV.ANOVA Using the above results, analysis of variance (ANOVA) is done on MINITAB 17. The results of ANOVA for surface roughness shown in table. Factor DF AdjSS Adj MS f- Value Table 5: Analysis of Variance for deformation. It can be seen from ANOVA table that for the Deformation (D), the contribution of mass is (9 %), Strain is (3.5%) and stress is (0.9%).The mass is having more influence on the deformation. Response Table for Signal to Noise Ratios (Smaller is better) Level Stress Strain Mass Delta Rank Table 6: Response Table for S-N ratio p-value A B C error total 8 All Rights Reserved 241

6 III. REGRESSION ANALYSIS A simple regression analysis can show that the relation between an independent variable X and a dependent variable Y is linear, using the simple linear regression equation Y= a + bx (where a and b are constants). Multiple regressions will provide an equation that predicts one variable from two or more independent variables Y=a+bX1+cX2+dX3+. The technique can show what proportion of variance between variables is due to the dependent variable, and what proportion is due to the independent variables. The relation between the variables can be illustrated graphically, or more usually using an equation. The net effects of the program under evaluation can be assessed using regression analysis, by attributing part of the changes observed to explanatory variables, while the remaining effects are attributed to the program. For this reason, regression analysis is useful in ex-post evaluation, to determine the net impact of the program. With the S/N and ANOVA analyses, the optimal combination of the process parameters can be predicted. Finally, a confirmation experiment is conducted to verify the optimal process parameters obtained from the parameter design. Regression Equation: D= X Y Z D ==> Deformation X ==> Stress Y ==> Strain Z ==> Mass Stress- X, Strain- Y,Mass- Z In this model, the criterion variable is the Deformation (D) and the predictor variables are Stress, Strain & Mass. Because these variables are controllable, they can be used to predict the deformation which will then enhance product quality. A commercial statistical package Minitab 17.0 was used to draw the regression equation. V.VALIDATION THROUGH TAGUCHI TECHNIQUE The experimental confirmation test is the final step in verifying the results drawn based on Taguchi s design approach. Numbers of confirmation tests are conducted and the observations are recorded below as Optimized parameters with validated result for deformation Optimized Parameters Component Stress (X) Strain (Y) Mass(Z) Deformation Knuckle joint Table 8. Taguchi Validation All Rights Reserved 242

7 By Minitab software, regression equation is: International Journal of Recent Trends in Engineering & Research (IJRTER) D = X Y Z = (132.92) (556) (2.204) = mm From the above conformation tests it is clear that the actual values of deformation are very close to the theoretical values. VI.RESULT Deformation (mm) Difference % For FEA and Experimental Component Theorotical Value( DOE) FEA Knuckle joint % Table 9: Summary of results % Error = [D (DOE) - D (FEA) ] / D (DOE) = [ ] / = 1.83 % VII.CONCLUSION 1. From results of finite element analysis it is observed that the maximum stress value is within the safety limit. The maximum displacement value is also very less. 2. It has been established that Taguchi analysis is an effective optimization technique. 3. Mathematical model for optimizing the deformation by design of experiment is identified by Taguchi method considering Stress, strain and mass. 4. Strain values for both FEA and Experimental method are within yield limit of material and having good relation with each other. REFERENCE I. Krishankant, Jatin Taneja, Mohit Bector, Rajesh Kumar, Application of Taguchi Method for Optimizing Turning Process by the effects of Machining Parameters, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: , Volume-2, Issue-1, October 2012 II. Pankaj Dulani et.al, Diameter and Spiral Thickness Optimization of Knuckle Joint Using Neural Network, International Journal of Science and Research (IJSR) ISSN (Online): III. Sangamesh B. Herakal et.al, Structural Static Analysis Of Knuckle Joint, International Journal of Engineering Research and General Science Volume 4, Issue 2, March-April, 2016, ISSN IV. Mahesh P. Sharma et.al, Static Analysis of Steering Knuckle and Its Shape Optimization, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-issn: , p-issn: X V. Kamlesh Lalasaheb Chavan et.al, Mass Reduction For Steering Knuckle Arm In A Suspension System Through Topology Optimization In CAE,International Journal of Scientific Research and Management Studies (IJSRMS) ISSN: Volume 1 Issue 11. VI. Sourav Das et.al, Analysis of Knuckle Joint of 30C8 Steel for Automobile Application, International Journal of Engineering Research & Technology (IJERT)Vol. 3 Issue 1, January 2014, IJERTIJERT, ISSN: VII. Shaik.John Bhasha et.al, Modeling and Analysis of Knuckle Joint, International Journal & Magazine of Engineering, Technology, Management and Research Journal, ISSN No: All Rights Reserved 243