scholarly journals The optimization of accuracy and efficiency for multistage precision grinding process with an improved particle swarm optimization algorithm

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989350
Author(s):  
Zhanying Chen ◽  
Xuekun Li ◽  
Zongyu Zhu ◽  
Zeming Zhao ◽  
Liping Wang ◽  
...  

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%.

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.


Sign in / Sign up

Export Citation Format

Share Document