The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm
For metal rolling, the quality of final rolled productions (for instance, metal sheets and metal foils) is affected by steel roll’s cylindricity. In roll grinding process, grinding parameters, which typically involve multiple substages, determine the steel roll’s quality and the grinding efficiency....
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2020-01-01
|
Series: | International Journal of Advanced Robotic Systems |
Online Access: | https://doi.org/10.1177/1729881419893508 |
id |
doaj-2d28839ef9544069a9bba477027eadbf |
---|---|
record_format |
Article |
spelling |
doaj-2d28839ef9544069a9bba477027eadbf2020-11-25T03:42:26ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142020-01-011710.1177/1729881419893508The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithmZhanying Chen0Xuekun Li1Zongyu Zhu2Zeming Zhao3Liping Wang4Sheng Jiang5Yiming Rong6 State Key Lab of Tribology, Tsinghua University, Beijing, China State Key Lab of Tribology, Tsinghua University, Beijing, China Hiecise Precision Equipment Co., Ltd, Suzhou, China Hiecise Precision Equipment Co., Ltd, Suzhou, China State Key Lab of Tribology, Tsinghua University, Beijing, China Hiecise Precision Equipment Co., Ltd, Suzhou, China Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaFor metal rolling, the quality of final rolled productions (for instance, metal sheets and metal foils) is affected by steel roll’s cylindricity. In roll grinding process, grinding parameters, which typically involve multiple substages, determine the steel roll’s quality and the grinding efficiency. In this article, a modified particle swarm optimization was presented to dispose of roll grinding multi-objective optimization. The minimization of steel roll’s cylindrical error and maximization of grinding efficiency were optimization objectives. To build the correlation between grinding parameters and cylindrical error, the response surface model of cylindrical error was regressed from the operation data of machine tool. The improved particle swarm optimization was employed to the roll grinding parameter optimization, and the optimal compromise solutions between grinding efficiency and cylindrical error were obtained. Based on the optimal compromise solutions, engineers or computer were capable to determine the corresponding most efficient roll grinding parameters according to the requirement of the final cylindrical error specification. To validate the efficacy of the improved particle swarm optimization, the validation experiment was carried out on the practical roll grinding operation. The error between the calculated optimized cylindrical error and experimental cylindrical error is less than 7.73%.https://doi.org/10.1177/1729881419893508 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhanying Chen Xuekun Li Zongyu Zhu Zeming Zhao Liping Wang Sheng Jiang Yiming Rong |
spellingShingle |
Zhanying Chen Xuekun Li Zongyu Zhu Zeming Zhao Liping Wang Sheng Jiang Yiming Rong The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm International Journal of Advanced Robotic Systems |
author_facet |
Zhanying Chen Xuekun Li Zongyu Zhu Zeming Zhao Liping Wang Sheng Jiang Yiming Rong |
author_sort |
Zhanying Chen |
title |
The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm |
title_short |
The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm |
title_full |
The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm |
title_fullStr |
The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm |
title_full_unstemmed |
The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm |
title_sort |
optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm |
publisher |
SAGE Publishing |
series |
International Journal of Advanced Robotic Systems |
issn |
1729-8814 |
publishDate |
2020-01-01 |
description |
For metal rolling, the quality of final rolled productions (for instance, metal sheets and metal foils) is affected by steel roll’s cylindricity. In roll grinding process, grinding parameters, which typically involve multiple substages, determine the steel roll’s quality and the grinding efficiency. In this article, a modified particle swarm optimization was presented to dispose of roll grinding multi-objective optimization. The minimization of steel roll’s cylindrical error and maximization of grinding efficiency were optimization objectives. To build the correlation between grinding parameters and cylindrical error, the response surface model of cylindrical error was regressed from the operation data of machine tool. The improved particle swarm optimization was employed to the roll grinding parameter optimization, and the optimal compromise solutions between grinding efficiency and cylindrical error were obtained. Based on the optimal compromise solutions, engineers or computer were capable to determine the corresponding most efficient roll grinding parameters according to the requirement of the final cylindrical error specification. To validate the efficacy of the improved particle swarm optimization, the validation experiment was carried out on the practical roll grinding operation. The error between the calculated optimized cylindrical error and experimental cylindrical error is less than 7.73%. |
url |
https://doi.org/10.1177/1729881419893508 |
work_keys_str_mv |
AT zhanyingchen theoptimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT xuekunli theoptimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT zongyuzhu theoptimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT zemingzhao theoptimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT lipingwang theoptimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT shengjiang theoptimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT yimingrong theoptimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT zhanyingchen optimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT xuekunli optimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT zongyuzhu optimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT zemingzhao optimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT lipingwang optimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT shengjiang optimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm AT yimingrong optimizationofaccuracyandefficiencyformultistageprecisiongrindingprocesswithanimprovedparticleswarmoptimizationalgorithm |
_version_ |
1724525091375546368 |