Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms

Authors:
R. Venkata Rao;P. J. Pawar
Affiliations:
Department of Mechanical Engineering, S.V. National Institute of Technology, Ichchanath, Surat, Gujarat - 395 007, India;Department of Production Engineering, K.K. Wagh Institute of Engineering Education and Research, Nashik - 422 003, Maharashtra, India
Venue:
Applied Soft Computing
Year:
2010

Citing 5
Cited 10

On the performance of artificial bee colony (ABC) algorithm

Applied Soft Computing
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Journal of Global Optimization
A novel hybrid immune algorithm for global optimization in design and manufacturing

Robotics and Computer-Integrated Manufacturing
Comparison among five evolutionary-based optimization algorithms

Advanced Engineering Informatics
A study of particle swarm optimization particle trajectories

Information Sciences: an International Journal

Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

Applied Soft Computing
A simulated annealing method based on a specialised evolutionary algorithm

Applied Soft Computing
Review: Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011)

Expert Systems with Applications: An International Journal
Artificial bee colony algorithm with self adaptive colony size

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms

Applied Soft Computing
An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process

Applied Soft Computing
Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures

International Journal of Applied Metaheuristic Computing
The evolutionary development of roughness prediction models

Applied Soft Computing
Free Pattern Search for global optimization

Applied Soft Computing
Weighted Average-Based Multi-Objective Optimization of Tube Spinning Process using Non-Traditional Optimization Techniques

International Journal of Swarm Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques.