| SU Xiao-yun,WANG Jian-xin,XIN Li-xia.Neural Network-based Prediction Model for Surface Roughness of Milled Marble[J],46(8):274-279 |
| Neural Network-based Prediction Model for Surface Roughness of Milled Marble |
| Received:May 07, 2017 Revised:August 20, 2017 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2017.08.044 |
| KeyWord:roughness marble neural network particle swarm prediction model |
| Author | Institution |
| SU Xiao-yun |
Baotou Iron and Steel Vocational Technical College, Baotou , China |
| WANG Jian-xin |
Inner Mongolia University of Science and Technology, Baotou , China |
| XIN Li-xia |
Baotou Iron and Steel Vocational Technical College, Baotou , China |
|
| Hits: |
| Download times: |
| Abstract: |
| The work aims to establish a prediction model for surface roughness of processed marble based upon BP neural network in particle swarm optimization method. Firstly, various cutting parameters were tested on the milling marble to obtain roughness value of the machined surface. Meanwhile, the particle swarm optimization (PSO) algorithm was improved to decrease inertia weight progressively in exponential form and increase disturbance coefficient of the velocity. The BP neural network was optimized in modified PSO method to establish a neural network prediction model for surface roughness of milled marble. Secondly, the prediction model was trained by using some test data so that network parameters obtained could predict the surface roughness more accurately. Finally, accuracy and reliability of the improved BP network prediction model were verified based upon other test data. For the prediction model adopting particle swarm optimization BP network algorithm method, normalized mean square error was 0.0501, the maximum relative error was 10.78% and error changed uniformly. For empirical formula model, the normalized mean square error was 0.1069, the maximum relative error was 39.64% and the error variation changed more significantly. Compared with empirical formula, the neural network model exhibits better prediction accuracy and robustness, and it is of certain reference value to achieve better surface roughness by selecting cutting parameters in a reasonable manner. |
| Close |
|
|
|