Use of least square support vector machine in surface roughness prediction model

Hua Dong; Dehui Wu; Haitao Su

doi:10.1117/12.716199

13 October 2006 Use of least square support vector machine in surface roughness prediction model

Hua Dong, Dehui Wu, Haitao Su

Proceedings Volume 6280, Third International Symposium on Precision Mechanical Measurements; 628022 (2006) https://doi.org/10.1117/12.716199
Event: Third International Symposium on Precision Mechanical Measurements, 2006, Urumqi, China

Abstract

This paper aims to introduce a novel model into prediction field for surface roughness in machining process, and report the results of comparison between the novel model and the other two prediction models in the experiments that have been examined. The novel model is based on least square support vector machine (LS-SVM), while the other two models are based on BP neural network and standard support vector machine (SVM) respectively. In the study, 54 groups of data about surface roughness and four kinds of parameters were obtained by full factorial experiments. And then, the data were analyzed by contrast experiments: set up prediction models with BP neural networks, standard SVM and LS-SVM respectively. The results have indicated that the mean deviation of LS-SVM model is only about 25% of that of SVM method, and 2~3 orders smaller than that of BP method. Furthermore, it takes the least time to set up the models by LS-SVM model among these approaches. In summing up it may be stated that the proposed model is faster in speed, higher in accuracy, and more suitable for prediction of surface roughness.

Citation Download Citation

Hua Dong, Dehui Wu, and Haitao Su "Use of least square support vector machine in surface roughness prediction model", Proc. SPIE 6280, Third International Symposium on Precision Mechanical Measurements, 628022 (13 October 2006); https://doi.org/10.1117/12.716199

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available