Abstract
In this study, the Taguchi method is used to find the optimum process parameters for maximum expansion of tube ends. The various process parameters namely the punch/die cone angle, the expansion ratio and the friction conditions are taken as the input process condition and the output; the maximum radial displacement is critically analyzed. Radial expansion is the increase in diameter of the tube by the die measured on the top most portion of the tube across its circumference. The optimal combination of the process parameters is obtained through the signal to noise ratio (S/N) analysis and the analysis of variance (ANOVA) methods. The parameters that affect the process are determined using Taguchi method and the most significant process parameters and their percentage contribution was determined by using ANOVA technique. Among all the three process parameters considered, it is found that the most significant factor is the die cone angle (α) and this factor contributes 43.56% on the total output response value while expansion ratio rp/r0 contributes 8.89% and the lubricant has contributed 38.59% on the total output. The experimental results are in acceptance with the predicted values for the 95% confidence interval and it is observed that the error is within the reasonable limit.
References
- Bing L, Nye TJ and Metzger Don R. Multi-objective optimization of forming parameters for tube hydroforming process based on the Taguchi method. International Journal of Advanced Manufacturing Technology, 2006; 28: 23 – 30.
- Dae-Cheol K, Dong-Hwan K and Byung-Min K. Application of artificial neural network and Taguchi method to preform design in metal forming considering workability. International Journal of Machine Tools & Manufacture, 1999; 39: 771 – 785.
- Hsin-Te L, Jie-Ren S and Yung-Kuang Y. Applications of Taguchi and design of experiments methods in optimization of chemical mechanical polishing process parameters. International Journal of Advanced Manufacturing Technology, 2008; 38: 674 – 682.
- Jurkovic Z, Jurkovic M and Buljan S. Optimization of extrusion force prediction model using different techniques. Journal of Achievements in Materials and Manufacturing Engineering, 2006; 17: 353 – 356.
- Padmanabhan R, Oliveira MC, Alves JL and Menezes LF. Influence of process parameters on the deep drawing of stainless steel. Finite Elements in Analysis and Design, 2007; 43: 1062 – 1067.
- Rossella S, Filippis LAC, Ludovico AD and Boghetich G. Application of Taguchi method for the multi objective optimization of aluminium foam manufacturing parameters. International Journal of Material Forming, 2010; 3: 1 – 5.
- Hsiang SH and Jer-Liang K. Applying ANN to predict the forming load and mechanical property of magnesium alloy under hot extrusion. International Journal of Advanced Manufacturing Technology, 2005; 26: 970 – 977.
- Wei F and Lin H. Multi-objective optimization of process parameters for the helical gear precision forging by using Taguchi method. Journal of Mechanical Science and Technology, 2011; 25: 519 – 1527.
Abstract
In this study, the Taguchi method is used to find the optimum process parameters for maximum expansion of tube ends. The various process parameters namely the punch/die cone angle, the expansion ratio and the friction conditions are taken as the input process condition and the output; the maximum radial displacement is critically analyzed. Radial expansion is the increase in diameter of the tube by the die measured on the top most portion of the tube across its circumference. The optimal combination of the process parameters is obtained through the signal to noise ratio (S/N) analysis and the analysis of variance (ANOVA) methods. The parameters that affect the process are determined using Taguchi method and the most significant process parameters and their percentage contribution was determined by using ANOVA technique. Among all the three process parameters considered, it is found that the most significant factor is the die cone angle (α) and this factor contributes 43.56% on the total output response value while expansion ratio rp/r0 contributes 8.89% and the lubricant has contributed 38.59% on the total output. The experimental results are in acceptance with the predicted values for the 95% confidence interval and it is observed that the error is within the reasonable limit.
References
- Bing L, Nye TJ and Metzger Don R. Multi-objective optimization of forming parameters for tube hydroforming process based on the Taguchi method. International Journal of Advanced Manufacturing Technology, 2006; 28: 23 – 30.
- Dae-Cheol K, Dong-Hwan K and Byung-Min K. Application of artificial neural network and Taguchi method to preform design in metal forming considering workability. International Journal of Machine Tools & Manufacture, 1999; 39: 771 – 785.
- Hsin-Te L, Jie-Ren S and Yung-Kuang Y. Applications of Taguchi and design of experiments methods in optimization of chemical mechanical polishing process parameters. International Journal of Advanced Manufacturing Technology, 2008; 38: 674 – 682.
- Jurkovic Z, Jurkovic M and Buljan S. Optimization of extrusion force prediction model using different techniques. Journal of Achievements in Materials and Manufacturing Engineering, 2006; 17: 353 – 356.
- Padmanabhan R, Oliveira MC, Alves JL and Menezes LF. Influence of process parameters on the deep drawing of stainless steel. Finite Elements in Analysis and Design, 2007; 43: 1062 – 1067.
- Rossella S, Filippis LAC, Ludovico AD and Boghetich G. Application of Taguchi method for the multi objective optimization of aluminium foam manufacturing parameters. International Journal of Material Forming, 2010; 3: 1 – 5.
- Hsiang SH and Jer-Liang K. Applying ANN to predict the forming load and mechanical property of magnesium alloy under hot extrusion. International Journal of Advanced Manufacturing Technology, 2005; 26: 970 – 977.
- Wei F and Lin H. Multi-objective optimization of process parameters for the helical gear precision forging by using Taguchi method. Journal of Mechanical Science and Technology, 2011; 25: 519 – 1527.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | March 27, 2016 |
| Published in Issue | Year 2012 Volume: 1 Issue: 2 |