| Issue |
E3S Web Conf.
Volume 309, 2021
3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|
|
|---|---|---|
| Article Number | 01165 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202130901165 | |
| Published online | 07 October 2021 | |
Optimization of Dressing Parameters for Minimum Surface Roughness and Maximum Material Removal Rate in Internal Grinding of SKD11 Tool Steel
1 University of Economics - Technology for Industries, Vietnam
2 Thai Nguyen University of Technology, Thai Nguyen, Vietnam
3 Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
4 Vinh Long University of Technology Education, Vietnam
5 Thai Nguyen Industrial College, Vietnam
* Corresponding author: lexuanhung@tnut.edu.vn
This paper introduces a study on multi-objective optimization of dressing parameters in internal grinding of SKD 11 tool steel using Grey based Taguchi method. The L27 orthogonal array of the Taguchi method was selected to design the experiments. The input parameters of the dressing process are the depth of fine, the time of fine dressing, the depth of coarse dressing, the time of coarse dressing, non-feeding dressing, and dressing feed rate. The output factors are surface roughness (SR) and material removal rate (MRR). A grey relation grade was determined by using the signal-to-noise ratio. The ANOVA was applied to find out the effect of input factors on the grey relation grade. In conclusion, the fine dressing times is the parameter that has the strongest impact on multiple performance characteristics, followed by the coarse dressing times. Also, the optimum dressing parameters to get minimum SR and maximum MRR is the depth of coarse dressing of 0.03mm, the time of coarse dressing of 2 times, the depth of fine dressing of 0.01 mm, the time of fine dressing of 2 times, non-feeding dressing of 2 times, and dressing feed rate of 1.2mm/min.
Key words: Internal grinding / dressing parameters / multi-objective optimization / SKD11
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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