Heat Treatment Parameters to Optimize Hardness Behavior of Carbon Steel Using Taguchi Technique
S. Krishnamoorthi1, D. Dinesh2, R. Karthikeyan3, G. Manikandan4
1S. Krishnamoorthi, Assistant Professor, Department of Mechanical Engineering, Chennai Institute of Technology, Chennai (Tamil Nadu), India.
2D. Dinesh, Assistant Professor, Department of Mechanical Engineering, Chennai Institute of Technology, Chennai (Tamil Nadu), India.
3R. Karthikeyan, Assistant Professor, Department of Mechanical Engineering, Chennai Institute of Technology, Chennai (Tamil Nadu), India.
4G. Manikandan, Assistant Professor, Department of Mechanical Engineering, Madha Institute of Engineering and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 19 May 2019 | Revised Manuscript received on 05 June 2019 | Manuscript Published on 15 June 2019 | PP: 10-14 | Volume-8 Issue-1S2 May 2019 | Retrieval Number: A00030581S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are so many engineering materials exists in this Universe. However, here we are discussing steel. We go through the different parameter, which is used in this experiment, for instance, heat treatment temperature. The selected material was medium carbon steel .the maximum temperature is around 1370 degrees so I selected the three levels of temperatures below the melting point i.e. 800, 900, 1000. The 800 degrees the heating time is 1hr, the 900degrees the heating time is 1.15hr, the 1000 degrees the heating time is 1.30hr. The hardness value is good at the 1000 degrees i.e. is 80 its coolant is salt water. The method was used in this process is the Taguchi method with the L9 array with 3 levels and 4 factors. The result and the calculated values are drawn by using the Minitab app in this app the ANOVA type is the general linear model. The percentage of variation i.e. Rsqu is between the 0-100percent. The good Rsqu values are 90-100%. I got a value of 95.06%. Therefore, the chosen process parameters are good. The two types of graphs are plotted i.e. mean of signal noise ratio and main effect plot for signal noise ratio, the next graph is mean of means and main effect plot for SN ratio.
Keywords: Heat Treatment, Melting Point, Temperature, Hardness, Taguchi Methods, Minitab, ANOVA, S/Nratio, Rsqu.
Scope of the Article: Heat Transfer