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

A surface roughness and support vector machine technology, applied in the field of grinding, can solve problems such as inappropriate parameter selection and reduced credibility of prediction results

Pending Publication Date: 2019-10-18
HUNAN UNIV OF SCI & TECH
View PDF1 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the randomness of the selection of support vector machine algorithm param

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Grinding surface roughness prediction method based on improved support vector machine algorithm
  • Grinding surface roughness prediction method based on improved support vector machine algorithm
  • Grinding surface roughness prediction method based on improved support vector machine algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0059] The present invention will be further described below in conjunction with the content of the present invention and the embodiments described in the accompanying drawings.

[0060] A kind of grinding surface roughness prediction method based on improved support vector machine algorithm of the present invention, it comprises the following steps:

[0061] Step 1. Design the experimental parameters and experimental plan of the grinding process. The grinding parameters are mainly the linear speed of the grinding wheel, the speed of the workpiece, and the grinding depth. Through multiple sets of experimental data, the surface roughness value is measured;

[0062] Step 2. The data obtained in Step 1 will be obtained. Using the idea of ​​cross-validation, divide the training set and test set, and perform normalization processing, and use the normalized grinding parameters and surface roughness as the input and output sample;

[0063] Step 3: Establish the GOA-SVM grinding surf...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/50G06N3/00G06N20/10B24B49/00
CPCG06N3/006G06N20/10B24B49/006G06F2111/10G06F30/20
Inventor 邓朝晖谷倩微李重阳刘涛吕黎曙
Owner HUNAN UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap