Christ University Institutional Repository

Optimization of Cutting Parameters in High Speed Machining of Thin Ribs

P, Pal Pandian and Sakthimurugan, K. and Raja, V.Prabhu (2010) Optimization of Cutting Parameters in High Speed Machining of Thin Ribs. Proceedings of International Conference on Advances in Industrial Engineering Applications . p. 6.

Full text not available from this repository.

Abstract

Machining of thin-walled parts is a key process in aerospace industry. Many components used in the aerospace industry are usually thin-walled structures. Because of their poor stiffness, thin-walled work pieces are very easy to deform under the action of cutting force in the process of cutting. Even in CNC milling, in which the tools are controlled exactly according to the contour of the thin-walled component, the wall will be thicker at the top and thinner at the root. In general, the surface dimensional error is induced mainly by the deflection of the work piece during milling, which does not remove the material as planned. The part deflection caused by the cutting force is difficult to predict and control. The main objective of this work is to achieve the minimum surface dimensional error which decreases the machining time. Therefore, the cutting parameters are to be optimized which enables the minimum possible surface dimensional error. The conditions required to achieve this in high speed milling process imply optimum cutting forces which in turn induce the part deflection. The present work is aimed at predicting cutting forces during machining and obtaining optimum cutting speed and feed rate. An Artificial Neural Network (ANN) predictive model is used to predict cutting forces during machining and Particle Swarm Optimization (PSO) algorithm is used to obtain optimum cutting speed and feed rate. The parts are modeled and effect of cutting force is applied and deflections of the work piece are found out using ANSYS. The ANN models and algorithms are developed using MATLAB.

Item Type:Article
Subjects:Publications > Publications by Faculty > Articles > Engineering
ID Code:1776
Deposited By:Knowledge Center Christ University
Deposited On:03 Aug 2012 09:54
Last Modified:03 Aug 2012 09:54

Repository Staff Only: item control page