Paper Title:
A Regression Neural Model for In-Process Surface Roughness Monitoring in End Milling Operations
  Abstract

The key element of the in-process surface roughness monitoring system is the decision-making model, which is utilized to analyze the input factors and then to generate a proper output. The success of the in-process monitoring system depends on the accuracy of the decision-making model. To increase the accuracy and reliability of model, it is important to reduce the variation of the inputs. To achieve this objective, an integration of regression and neural network was developed as a decision-making model in this research. In this integrated model, the regression model was applied as a filter to sort the input variables into groups. Furthermore, the grouped data was implemented to train and to generate different neural networks models to reduce the affection of input variation and increase the accuracy of the monitoring system. The input variables was first filtered by the threshold of regression model, and then analyzed by different neural networks model based on the filtered result. Finally, to evaluate the performance of the integrated model, the regression neural network and traditional neural networks were both developed for surface roughness monitoring system in an end milling operation to compare the accuracy of systems.

  Info
Periodical
Key Engineering Materials (Volumes 419-420)
Edited by
Daizhong Su, Qingbin Zhang and Shifan Zhu
Pages
369-372
DOI
10.4028/www.scientific.net/KEM.419-420.369
Citation
P. T. B. Huang, J. C. Chen, Y. T. Jou, "A Regression Neural Model for In-Process Surface Roughness Monitoring in End Milling Operations", Key Engineering Materials, Vols. 419-420, pp. 369-372, 2010
Online since
October 2009
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Somkiat Tangjitsitcharoen
Abstract:In order to realize an intelligent machine tool, an in-process monitoring system is developed to estimate the in-process surface roughness....
376
Authors: Somkiat Tangjitsitcharoen
Abstract:The objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by...
1958
Authors: Somkiat Tangjitsitcharoen, Angsumalin Senjuntichai
Chapter 4: Mechatronics and Information
Abstract:In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end...
2059
Authors: Siriwan Chanphong, Somkiat Tangjitsitcharoen
Chapter 6: Polymer Materials
Abstract:This research presents the development of the surface roughness prediction in the turning process of the plain carbon steel with the coated...
921
Authors: Vichaya Thammasing, Somkiat Tangjitsitcharoen
Chapter 1: Advanced Materials Engineering and Processing Technologies
Abstract:The purpose of this research is to develop the models to predict the average surface roughness and the surface roughness during the...
29