Big-data-mining-based tool wear state prediction method under variable working condition

A technology for tool wear and prediction methods, applied in manufacturing tools, metal processing equipment, measuring/indicating equipment, etc., can solve problems such as poor accuracy, and achieve the effect of improving accuracy, improving prediction accuracy, and reducing prediction errors

Active Publication Date: 2016-12-14
SHANNXI DIESEL ENGINE HEAVY IND +1
View PDF6 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of the poor accuracy of the existing tool wear state prediction methods, the

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
  • Big-data-mining-based tool wear state prediction method under variable working condition
  • Big-data-mining-based tool wear state prediction method under variable working condition
  • Big-data-mining-based tool wear state prediction method under variable working condition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] The specific steps of the tool wear state prediction method under variable working conditions based on big data mining in the present invention are as follows:

[0025] Firstly, the variable operating conditions are expressed in the form of operating condition vectors, and the changes in operating conditions are represented by the different values ​​of the attributes in the vector. The working conditions that affect tool wear can be divided into four categories, the tool itself, the machine tool, the workpiece, the processing parameters, the representation of the working condition vector and the specific attributes included are as follows.

[0026] Machine tool condition vector L=(P,T q , f max ,n max ,P r ,D p ), where P represents the power of the machine tool, and T q Indicates the spindle torque, f max Indicates the maximum feed rate, n max Indicates the maximum speed P r Indicates the positioning accuracy of the machine tool, D p Indicates the dynamic para...

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 discloses a big-data-mining-based tool wear state prediction method under a variable working condition. The method is used for solving the technical problem that an existing tool wear state prediction method is poor in accuracy. By means of the technical scheme, the large data technology is adopted, complete sample data under the variable working condition factor are obtained, the FFNN data mining method is improved, the incremental learning capacity is achieved, the new condition can be continuously fused, and the more accurate predication model is obtained. Due to consideration of the complete sample data affecting tool wear and incremental learning of the new tool state feature vector, the new condition is continuously fused and learnt, the more accurate prediction model is obtained, and the basis is provided for further analysis of relevant factors affecting tool wear. Compared with the prior art, the newly-added condition is learnt, and accuracy of the tool wear state predication under the variable working condition is improved. Through detection, according to the minimum error result, the err is equal to 0.03933, and the predication accuracy rate is improved by more than 96%.

Description

technical field [0001] The invention relates to a tool wear state prediction method, in particular to a tool wear state prediction method under variable working conditions based on big data mining. Background technique [0002] The prediction of tool wear state is of great significance to make full use of tool life, ensure the surface quality of workpieces, and reduce the damage caused by tool wear to machine tools and other equipment. The existing tool wear prediction methods mainly include: machine learning method based on signal characteristics in the cutting process and empirical formula method based on experimental verification. [0003] The document "Prediction of Tool VB Value Based on Principal Component Analysis and BP Neural Network, Journal of Beijing University of Aeronautics and Astronautics, 2011, Vol.37(3), p364-367" discloses a tool wear method based on an improved BP neural network. predictive algorithm. In this method, the acoustic emission signal, cuttin...

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): B23Q17/09
CPCB23Q17/0995
Inventor 雷军郭兴安周竞涛王明微贠虎臣李恩明符博峰赵明乔卫杰
Owner SHANNXI DIESEL ENGINE HEAVY IND
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