|
Abstract
|
Article Information:
A New Tool Wear Monitoring Method Based on Ant Colony Algorithm
Qianjian Guo, Shanshan Yu and Xiaoni Qi
Corresponding Author: guo qianjian
Submitted: December 20, 2012
Accepted: January 25, 2013
Published: June 10, 2013 |
Abstract:
|
Tool wear prediction is a major contributor to the dimensional errors of a work piece in precision
machining, which plays an important role in industry for higher productivity and product quality. Tool wear
monitoring is an effective way to predict the tool wear loss in milling process. In this paper, a new bionic prediction
model is presented based on the generation mechanism of tool wear loss. Different milling conditions are estimated
as the input variables, tool wear loss is estimated as the output variable, neural network method is proposed to
establish the mapping relation and ant algorithm is used to train the weights of BP neural networks during tool wear
modeling. Finally, a real-time tool wear loss estimator is developed based on ant colony alogrithm and experiments
have been conducted for measuring tool wear based on the estimator in a milling machine. The experimental and
estimated results are found to be in satisfactory agreement with average error lower than 6%.
Key words: Ant colony algorithm, CNC milling machine, tool wears monitoring, online estimation, , ,
|
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
Qianjian Guo, Shanshan Yu and Xiaoni Qi, . A New Tool Wear Monitoring Method Based on Ant Colony Algorithm. Research Journal of Applied Sciences, Engineering and Technology, (02): 334-338.
|
|
|
|
 |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
 |
| Information |
|
|
|
|
| Sales & Services |
|
|
|
|
