Open AccessDensity prediction of selective laser sintering parts based on support vector regression
Author(s) -
Cai Cong-Zhong,
Pei Jun-Fang,
Wen Yu-Feng,
Zhu Xing-Jian,
Tingting Xiao
Publication year - 2009
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.58.8
Subject(s) - particle swarm optimization , support vector machine , artificial neural network , selective laser sintering , computer science , materials science , laser , mode (computer interface) , sintering , pattern recognition (psychology) , artificial intelligence , biological system , algorithm , optics , composite material , physics , operating system , biology
The support vector regression SVR approach combined with particle swarm optimization for parameter optimization, is proposed to establish a model for estimating the density of selective laser sintering parts under processing parameters, including layer thickness, hatch spacing, laser power, scanning speed, ambient temperature, interval time and scanning mode. A comparison between the prediction results and the results from the BP neural networks strongly supports that the internal fitting capacity and prediction accuracy of SVR model are superior to those of BP neural networks under the identical training and test samples; the generation ability of SVR model can be efficiently improved by increasing the number of training samples. The minimum error value is provided by leave-one-out cross validation test of SVR. These results suggest that SVR is an effective and powerful tool for estimating the density of selective laser sintering parts.