Optimisation of AWJM Process Parameters for Machining Granite using PCA Methodology
Lingaraj N1, S K Rajesh Kanna2, P Sivasankar3, Ilayaperumal K4, Akash C5
1Lingaraj N, Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai (Tamil Nadu), India.
2S K Rajesh Kanna, Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai (Tamil Nadu), India.
3P Sivasankar, Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai (Tamil Nadu), India.
4Ilayaperumal K, Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai (Tamil Nadu), India.
5Akash C, Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 16 December 2019 | Manuscript Published on 30 December 2019 | PP: 530-534 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B10741292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1074.1292S219
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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: In Abrasive water jet machining, abrasive particles along with high pressure water are used to intrude on the work materials ranges from soft to hard materials using high velocity jet. The process parameters considered in this research for machining the granite are pressure, standoff-distance and cut quality. Experimental investigation had been carried out, in order to identify the impact of varying the input machining parameters on the results like kerf angle, material removal rate and roughness of the machined surface. In this study, Taguchi’s Multi response technique namely principal component analysis had been used to optimize the input parameters of the abrasive jet machine to obtain the desired outcome on granite work piece and also to foresee the best optimal input machining values of abrasive jet machining such as pressure, standoff-distance and cut quality. For each sequence of Taguchi L9 orthogonal array, sufficient number of experimentations had been carried out. Then with the help of principal component analysis, optimal process parameters that influence the granite machining characteristics have identified and to validate the experimentation, confirmation tests also been carried out with required combinations of array.
Keywords: AWJM, Taguchi’s Multi Response Method, ANOVA, PCA.
Scope of the Article: Manufacturing Processes