NDLI: Surface Hardness Intelligent Prediction in Milling Using Support Vector Regression

Content Provider	IEEE Xplore Digital Library
Author	Deh Wu
Copyright Year	2008
Description	Author affiliation: Key Lab. of Numerical Control ofJiangxi Province, Jiujiang Univ., Jiujiang (Deh Wu)
Abstract	Surface hardness is a major factor affecting the performance of a component. Understanding the influence of machining parameters on surface hardness is very important for the control of workpiece quality. In milling process development, it is highly desirable to predict the surface hardness of machined workpiece. For this purpose, a novel prediction model based on support vector regression (SVR) is developed to investigate the influence of processing parameters on the milled surface hardness. Firstly, the influence of spindle speed, feed rate, cutting depth and milling cutter on the milled surface hardness (from experimental data) are taken into account. Then the experimental procedure employed a full factorial design with these foresaid parameters as factors, each of first three terms at three levels and the last at two levels, are carried out and 54 groups of data are obtained. Furthermore, the data are analyzed by different experiments in contrast: SVR based models and BP based model respectively. The experimental results have indicated that SVR based model outperforms its main competitors-BP model in the limited training data, and the mean absolute error of which is only 20%-30% of the latter. Lastly, This prediction model provides a better understanding of the influence of milling conditions on machined surface hardness. In sum, the proposed model is faster in speed, higher in accuracy, and more suitable for prediction of the machined surface hardness.
Starting Page	188
Ending Page	192
File Size	531992
Page Count	5
File Format	PDF
ISBN	9780769533049
DOI	10.1109/ICNC.2008.258
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Design engineering Data analysis Laboratories Training data Milling Machining Artificial neural networks Predictive models Feeds Computer numerical control
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Intelligent Prediction of Surface Micro-hardness after Milling Based on Smooth Support Vector Regression

Time Series Prediction for Machining Errors Using Support Vector Regression

Robustness Analysis of Artificial Neural Networks and Support Vector Machine in Making Prediction

Bus travel time prediction model with ν - support vector regression

Prediction model of grind machining of engineering ceramics based on BP neural network

Prediction of burning rate of HTPB propellant by using support vector regression

Gross industrial output value prediction based on least squares support vector regression

Travel time prediction with support vector regression

Two Support Vector Machine Regression Models for Quality Variation Prediction

Surface Hardness Intelligent Prediction in Milling Using Support Vector Regression

Similar Documents

Intelligent Prediction of Surface Micro-hardness after Milling Based on Smooth Support Vector Regression

Time Series Prediction for Machining Errors Using Support Vector Regression

Robustness Analysis of Artificial Neural Networks and Support Vector Machine in Making Prediction

Bus travel time prediction model with ν - support vector regression

Prediction model of grind machining of engineering ceramics based on BP neural network

Prediction of burning rate of HTPB propellant by using support vector regression

Gross industrial output value prediction based on least squares support vector regression

Travel time prediction with support vector regression

Two Support Vector Machine Regression Models for Quality Variation Prediction

Surface Hardness Intelligent Prediction in Milling Using Support Vector Regression