Grinding surface roughness prediction method based on improved support vector machine algorithm

Abstract

The invention provides a grinding surface roughness prediction method based on an improved support vector machine algorithm. Parameters of a support vector machine algorithm are optimized by using a grasshopper algorithm, and the method specifically comprises the following steps: (1) designing grinding machining parameters and an experimental method, and collecting machining parameters in the grinding machining process; (2) dividing the data into a training set and a test set by using the thought of cross validation, and performing normalization processing; (3) enabling the grinding wheel linear speed, the workpiece speed and the grinding depth to serve as input parameters, enabling the surface roughness to serves as output parameters, and constructing a GOA-SVM prediction model for predicting the roughness of the grinding surface. Compared with other prediction grinding, the grinding surface roughness prediction method based on the improved support vector machine algorithm provided bythe invention can rapidly seek a globally optimal solution in a complex search space, has the characteristics of low cost, high precision and easiness in operation, and has a smaller error between aprediction value and a true value.

Description

technical field [0001] The invention belongs to the technical field of grinding, and in particular relates to a method for predicting the roughness of a grinding surface based on an improved support vector machine algorithm. Background technique [0002] Surface roughness is one of the important criteria to measure product quality. Due to the complex and changeable processing environment and the uncertain state of the grinding wheel, it is difficult to accurately control the grinding surface roughness during the grinding process. Roughness is closely related. Therefore, how to accurately and efficiently predict the grinding surface roughness is a key problem in the field of grinding processing. [0003] During the grinding process, there are many factors affecting the surface roughness, and these factors are coupled with each other, so there is a complex nonlinear relationship between the grinding process parameters and the surface roughness. Support vector machine (suppo...

AI Technical Summary

Problems solved by technology

However, due to the randomness of the selection of support vector machine algorithm parameters, improper parameter selection will reduce the credibility of the prediction results.

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