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Method, device, equipment and storage medium for predicting removal amount of grinding material

A technology for grinding materials and prediction methods, applied in grinding/polishing equipment, measuring devices, metal processing equipment, etc., can solve problems such as intractability, reduce processing costs, improve processing efficiency, and be easy to deploy.

Active Publication Date: 2021-11-16
HUAZHONG UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, even if the grinding ability has been greatly improved, metals with excellent performance, such as titanium alloys, are extremely difficult to machine

Method used

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  • Method, device, equipment and storage medium for predicting removal amount of grinding material
  • Method, device, equipment and storage medium for predicting removal amount of grinding material
  • Method, device, equipment and storage medium for predicting removal amount of grinding material

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] see figure 1 As shown, the present invention provides a flow chart of a grinding material removal prediction method, which specifically includes:

[0053] S10: Calibrate the acoustic emission sensor;

[0054] S20: fixing the acoustic emission sensor on the workpiece fixture;

[0055] S30: collecting the acoustic emission signal received by the acoustic emission sensor, measuring the material removal depth and converting it into a material removal amount;

[0056] S40: Process the collected acoustic emission signal by wavelet transform method and fast Fourier transform;

[0057] S50: extracting the characteristic value of the acoustic emission signal generated in the grinding process;

[0058] S60: Construct a nonlinear regression model of material removal amount according to the material removal amount and the eigenvalue, so as to realize online prediction of material removal amount and guide the subsequent grinding process.

[0059] Among them, S10: calibrate the a...

Embodiment 2

[0081] see figure 2 , which provides a flow chart of an integrated learning algorithm for predicting the removal amount of titanium alloy material removed by robotic abrasive belt grinding for the present invention.

[0082] Step S60 in the first embodiment constructs a nonlinear regression model of the material removal amount according to the material removal amount and the eigenvalue, so as to realize the online prediction of the material removal amount and guide the subsequent grinding process, specifically including: according to the The material removal amount and the eigenvalue construct the nonlinear regression model of the material removal amount based on integrated learning, wherein the integrated learning algorithm includes:

[0083] S61: Initialize weight distribution;

[0084] S62: Calculate the training set sample error value;

[0085] S63: Calculate the regression error rate of the base learner;

[0086] S64: Calculate the weight coefficient of the base learn...

Embodiment 3

[0109] see Figure three , the present invention provides a verification process for the prediction method of grinding material removal amount;

[0110] Based on an embodiment of a method for predicting the amount of grinding material removal, further, a verification process for the method for predicting the amount of grinding material removal is proposed. Specifically include:

[0111] S10: Calibrate the acoustic emission sensor;

[0112] S20: fixing the acoustic emission sensor on the workpiece fixture;

[0113] S30: collecting the acoustic emission signal received by the acoustic emission sensor, measuring the material removal depth and converting it into a material removal amount;

[0114] S40: Process the collected acoustic emission signal by using a wavelet transform method and a fast Fourier transform algorithm;

[0115] S50: extracting the characteristic value of the acoustic emission signal generated in the grinding process;

[0116] S60: Construct a nonlinear re...

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Abstract

The invention provides a method for predicting the amount of grinding material removal, comprising: calibrating the acoustic emission sensor; fixing the acoustic emission sensor on the workpiece fixture; collecting the acoustic emission signal received by the acoustic emission sensor, and measuring the removal depth of the material And convert it into the amount of material removal; process the collected acoustic emission signal by wavelet transform method and fast Fourier transform; extract the eigenvalue of the acoustic emission signal in the grinding process; construct The nonlinear regression model of the material removal amount realizes the online prediction of the material removal amount and guides the subsequent grinding process. Through the above scheme, it is possible to predict and evaluate the material removal amount of each path in the robotic abrasive belt grinding process, reduce downtime, improve processing efficiency, and reduce processing costs. In addition, the embodiments of the present invention provide a grinding material removal amount prediction device, device and storage medium.

Description

technical field [0001] The invention belongs to the technical field of robot workpiece grinding, and more particularly relates to a method, device, equipment and storage medium for predicting the removal amount of grinding materials. Background technique [0002] As an important workpiece processing method, grinding has been developed for decades, and grinding has a vital influence on the surface quality of workpieces. Initially, the grinding of workpieces with complex geometries usually relied on human labor and CNC machining, which was not only time-consuming and laborious, but also difficult to achieve the required precision. In recent years, intelligent robotic grinding systems have been widely introduced into precision manufacturing, and exhibit excellent performances of flexibility, high efficiency, and high precision. In addition, robotic belt grinding offers highly controlled material removal. [0003] However, even if the grinding ability has been greatly improved...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B24B49/00B24B49/02G01B17/00
CPCB24B49/003B24B49/006B24B49/02G01B17/00
Inventor 严思杰白贺彬徐小虎刘奇丁汉
Owner HUAZHONG UNIV OF SCI & TECH