Journal
NEUROCOMPUTING
Volume 198, Issue -, Pages 35-39Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2015.08.124
Keywords
Surface roughness prediction; Least squares support vector machine (LS-SVM); Neural networks; Levenberg-Marquardt algorithm; ANOVA Machined surfaces
Categories
Funding
- Direct For Education and Human Resources
- Division Of Human Resource Development [1505509, 1531014] Funding Source: National Science Foundation
- Direct For Education and Human Resources
- Division Of Human Resource Development [1622811] Funding Source: National Science Foundation
Ask authors/readers for more resources
An effective least squares support vector machine (LS-SVM)-based approach was developed to predict the surface roughness in machined surface. The real AISI4340 steel and AISID2 steel data set was used to conduct the experiments. The analysis of variance (ANOVA) was used to validate the assumption of normal distribution, as well as the independent distribution of the errors. For the neural networks model, with 70%, 15%, and 15% of data as training, validation, and testing data, respectively, the best validation error is 0.0097343. The training error is 9.08888e-4 and the testing error is 1.09510e-1 accordingly. NN methods also discovered the correlation between the predicted surface roughness (Ra) and the actual surface roughness in the form of predicted Ra congruent to 0.41*Actual Ra+0.2. The LS-SVM performance was also compared to the analysis of variance (ANOVA) method, and neural networks model trained by Leven berg-Marquardt algorithm. The experimental results showed that the proposed LS-SVM algorithm produced a determination coefficient of =0.9439, which is higher than the ANOVA and NN results of 0.1917 and 0.7266. (C) 2016 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available