Investigation of the Machinability Characteristics of GFRP / Epoxy Composites Using Taguchi Methodology

Author:

Vasudevan Hari1,Deshpande Naresh1,Rajguru Ramesh1

Affiliation:

1. J. Sanghvi College of Engineering

Abstract

Many glass fiber reinforced plastic (GFRP) composite components made from primary melt processes require additional machining to meet the requirements of assembly and accurate dimensional tolerances. Importance of woven fabric based glass fibre reinforced composites is widely known in many industrial applications. However, very little is known about machinability of these composites. Cutting force is treated as one of the primary measures for determining the machinability of any material.This paper presents an investigation into the longitudinal turning of woven fabric and epoxy based GFRP composites, using polycrystalline diamond tool, so as to analyze the effect of cutting parameters and insert radius on the cutting force. The force was measured through longitudinal turning, according to the experimental plan, as developed on the basis of Taguchi methodology. The signal to noise ratio and analysis of variance were applied to the experimental data, in order to determine the effect of the process variables on tangential cutting force. Statistical results indicated that the cutting force is significantly influenced (at a 95% confidence level) by feed rate, followed by depth of cut, whereas, cutting speed and insert radius have a smaller influence. The cutting force also increases with the increase in feed rate and depth of cut.

Publisher

Trans Tech Publications, Ltd.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fabrication of Fibre-Reinforced Composites Using Vacuum Infusion Process and Testing;Proceedings of International Conference on Intelligent Manufacturing and Automation;2023

2. Optimization of Machining Parameters for Surface Roughness in the End Milling of Hybrid Composite Using Response Surface Methodology;Proceedings of International Conference on Intelligent Manufacturing and Automation;2023

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