Modelling of surface roughness and studying of optimal machining position in side milling

2021 ◽  
Author(s):  
Jinfeng Bai ◽  
Huiying Zhao ◽  
Lingyu Zhao ◽  
Mingchen Cao ◽  
Duanzhi Duan
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization Algorithm ◽  
Surface Generation ◽  
The Novel ◽  
Dynamic Effects ◽  
Computational System ◽  
Swarm Optimization ◽  
Side Milling ◽  
Milling Operations ◽  
Novel Model

Abstract In this work, a theoretical analysis of surface generation numerical model is presented to predict the surface roughness achieved by side milling operations with cylindrical tools. This work is focused on the trajectory of tools with two teeth by influencing of tool errors such as radial runouts, as well as straightness with dynamic effects. A computational system was developed to simulate roughness topography in contour milling with cylindrical tool. Finally, the PSO (particle swarm optimization) algorithm is employed to find the optimal machining position for the best surface roughness. Experimental data is satisfied with the the novel pretiction model for the tooth’s trajectory, and the the final prediction accuracy is high enough, i.e. that the prediction surface roughness. Low prediction surface roughness error (1.37 ~ 15.04%) and position error (0.95 ~ 1.25 mm) indicate effectiveness of the model built in this work. The novel model may be used to determine the variation in surface roughness.

scholarly journals A Velocity-Combined Local Best Particle Swarm Optimization Algorithm for Nonlinear Equations

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhigang Lian ◽  
Songhua Wang ◽  
Yangquan Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Equations ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
The Given ◽  
Novel Algorithm

Many people use traditional methods such as quasi-Newton method and Gauss–Newton-based BFGS to solve nonlinear equations. In this paper, we present an improved particle swarm optimization algorithm to solve nonlinear equations. The novel algorithm introduces the historical and local optimum information of particles to update a particle’s velocity. Five sets of typical nonlinear equations are employed to test the quality and reliability of the novel algorithm search comparing with the PSO algorithm. Numerical results show that the proposed method is effective for the given test problems. The new algorithm can be used as a new tool to solve nonlinear equations, continuous function optimization, etc., and the combinatorial optimization problem. The global convergence of the given method is established.

A Lattice Discrete Model for Intensive RFID Reader Deployment

2013 ◽  
Vol 340 ◽  
pp. 502-506
Author(s):  
Chao Chen ◽  
Shi Jie Zhou ◽  
Jia Qing Luo ◽  
Yan Pan Chen
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Coverage Problem ◽  
Matrix Operation ◽  
High Coverage ◽  
Swarm Optimization ◽  
Discrete Method ◽  
Rfid Reader ◽  
Np Problem ◽  
Novel Model ◽  
High Coverage Rate

In an intensive RFID reader environment, multiple RFID reader are deployed together to cover a pointed area. In such intensive RFID reader application, it needs to determine how many readers are enough to cover the expect area and calculate the position of readers. However, the coverage of multiple readers is a NP problem. Therefore, it needs an approximate approach to optimize the coverage. In this paper, we proposed a lattice decentralized approach to model the coverage problem of intensive RFID reader deployment. In our novel model, both the deployment area and the reader reading region are discretized to a lattice and described by a matrix. Then, the coverage is easily calculated by matrix operation. In order to test our discrete method, we propose a heuristic algorithm to deploy readers based on the PSO (particle swarm optimization) algorithm. The simulations show that the proposed algorithm can cover an irregular or regular area with a high coverage rate and a low overlapping rate.

scholarly journals An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhen-Lun Yang ◽  
Angus Wu ◽  
Hua-Qing Min
Keyword(s):  
Particle Swarm Optimization ◽  
Unconstrained Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
State Of The Art ◽  
Particle Swarm ◽  
Breeding Strategy ◽  
The Novel ◽  
Swarm Optimization ◽  
Local Optima ◽  
Search Capability

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate.

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