Prediction of Abrasive Belt Wear Based on BP Neural Network

Yuanxun Cao,Xin Wang,Ji Zhao,Bowen Liu,Xingtian Qu

doi:10.3390/machines9120314

Abstract

Abrasive belt grinding is the key technology in high-end precision manufacturing field, but the working condition of abrasive particles on the surface of the belt will directly affect the quality and efficiency during processing. Aiming at the problem of the inability to monitor the wearing status of abrasive belt in real-time during the grinding process, and the challenge of time-consuming control while shutdown for detection, this paper proposes a method for predicating the wear of abrasive belt while the grinding process based on back-propagation (BP) neural network. First, experiments are carried out based on ultra-depth-of-field detection technology, and different parameter combinations are used to measure the degree of abrasive belt wear. Then the effects of different grinding speeds, different contact pressures, and different work piece materials on the abrasive belt wear rate are obtained. It can be concluded that the abrasive belt wear rate gradually increases as the grinding speed of the abrasive belt increases. With the increase of steel grade, the hardness of the steel structure increases, which intensifies the abrasive belt wear. As the contact pressure increases, the pressure on a single abrasive particle increases, which ultimately leads to increased wear. With the increase of contact pressure, the increase of the wear rate of materials with higher hardness is greater. By utilizing the artificial intelligence BP neural network method, 18 sets of experiment data are used for training BP neural network while 9 sets of data are used for verification, and the nonlinear mapping relationship between various process parameter combinations such as grinding speed, contact pressure, workpiece material, and wear rate is established to predict the wear degree of abrasive belt. Finally, the results of verification by examples show that the method proposed in this paper can fulfill the purpose of quickly and accurately predicting the degree of abrasive belt wear, which can be used for guiding the manufacturing processing, and greatly improving the processing efficiency.

Highlights

Since the 1960s, the technology of abrasive belt grinding has been greatly developed
Abrasive belt will inevitably wear in the process of grinding materials, and the wear of abrasive belt is the result of multiple factors, such as grinding pressure, abrasive belt speed, workpiece materials, etc., the wear forms of abrasive belt will be different in grinding different materials or under different grinding conditions
Considering that there is no literature to solve the above problems up to now, this paper proposes a method for predicting abrasive belt wear based on back-propagation (BP) neural network

Summary

Introduction

Since the 1960s, the technology of abrasive belt grinding has been greatly developed. In order to extend the service life of the abrasive belt, on the one hand, it is necessary to select an abrasive belt with stable grinding performance and high durability under the premise of meeting the grinding requirements, and optimize the grinding process to increase the wear. It is necessary to design a special abrasive belt grinding mechanism to increase the effective grinding area of the abrasive belt. The former is limited by abrasive belt manufacturing technology, while the latter is restricted by the space of polishing equipment, and both have certain limitations. It is necessary to accurately determine the degree of abrasive belt wear on the basis of the above requirements, avoid premature replacement when the abrasive belt still has the grinding ability, and at the same time prevent the continued service of the abrasive belt that is reaching its life limitation [4]

State of the Art

Abrasive Belt Wear Measurement Test

Test Results

Test Data Analysis