Open Access Peer-reviewed Research Article

Modeling & optimization of Ti6Al4V turning for sustainable shearing considering rake angle

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Mean S/N ratios for Ra
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Submitted   Sep 16, 2024
Published   Nov 20, 2024
Issue    Vol 3 No 1 (2024)
DOI   10.25082/RIMA.2024.01.004

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Amit Patil corresponding author
Department of Mechanical Engineering, MET’s Institute of Engineering, Nashik, Maharashtra, India
Sushil Ingle
Department of Mechanical Engineering, MET’s Institute of Engineering, Nashik, Maharashtra, India

Abstract

Titanium alloys, such as Ti6Al4V, have become increasingly prevalent in aerospace and biomedical industries owing to their exceptional mechanical properties and corrosion resistance. However, the machining of these alloys presents significant challenges including high tool wear, poor surface finish, and low productivity. This study focused on enhancing the machinability of Ti6Al4V during CNC turning using the Taguchi optimization method. This approach aims to identify the optimal cutting parameters that minimize the surface roughness, flank wear, and crater wear, thereby improving the overall machining performance. This study systematically investigated the influence of various cutting parameters on machining outcomes. The experimental results demonstrate that the Taguchi method effectively determines the optimal process parameters, leading to a significant reduction in surface roughness and tool wear. These findings highlight the potential of the Taguchi optimization technique for achieving improved machinability and sustainability in the machining of Ti6Al4V.

Keywords
Ti6Al4V, Taguchi optimization, rake angle, machinability

Article Details

Supporting Agencies
The authors thank the Department of Mechanical Engineering, Institute of Engineering, Bhujbal Knowledge City, Adgaon, Nashik for their kind help and technical support in carrying out this research.
How to Cite
Patil, A., & Ingle, S. (2024). Modeling & optimization of Ti6Al4V turning for sustainable shearing considering rake angle. Research on Intelligent Manufacturing and Assembly, 3(1), 118-128. https://doi.org/10.25082/RIMA.2024.01.004
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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