Experimental Study to Minimize The Burr Formation in Drilling Process With Artifical Neural Networks (ANN) Analysis

International Conference on Mechanical, Materials and Renewable Energy

机械制造;材料科学;能源学

Dey, B.^1 ; Mondal, N.^1 ; Mondal, S.^1

Department of Mechanical Engineering, Jalpaiguri Government Engineering College, Jalpaiguri

West Bengal

735102, India^1

关键词: Artifical neural network (ANN);    Burr formation;    Burr height;    Drilling process;    Experimental test;    Spindle speed;    Three parameters;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/377/1/012120/pdf
DOI  :  10.1088/1757-899X/377/1/012120

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In present work, drilling of a through a hole in an aluminium bar has been observed the formation of a burr. The unwanted material raised beyond the work piece called burr. The minimization of the burr is important for manufacturing aspect which reduces cost and increases the life of the product. In this paper drilling on aluminium work piece experimental test has been conducted three parameters drill diameter, Point Angle and spindle speed and each of the parameter three different level (maximum, intermediate and minimum)value has been chosen. The each set of an experiment the burr height and thickness has been measured. The effect of each parameter which reduces the burr height and thickness has been identified. In this paper, artificial neural networks (ANN) model are developed for comparining the experimental results.The ANN modeled values show very close matching with the experimental results.