A Novel Cloud Model Prediction for Surface Roughness Based on Multidimensional & Multi-rules Reasoning
Aiming at the problems of lower prediction accuracy and narrower prediction range for the common prediction method, a new surface roughness prediction method based on multidimensional&multi-rules reasoning of cloud model is put forward. Based on analyzing a large quantity of test data, the digital characteristic of a multidimensional cloud is represented and the generator on multidimensional&multi-rules for qualitative reasoning is designed firstly. Then through combining the reasoning rules in various modes, the accurate surface roughness prediction is realized with the cutting speed, feed rate and cut depth as the input conditions and the predicted roughness values as output, thus the variation law of quality of machined surface following milling parameters can be obtained. Finally, experimental results show that the presented method is more accurate with the average relative prediction error below to 4.78% in the same conditions. Compared with other prediction models, the presented model makes the information classification and accuracy prediction more precise, and the predicted range wider.
NETL
NSTL
万方数据
维普
