Overview of PSO for Optimizing Process Parameters of Machining
In the current trends of optimizing machining process parameters, various evolutionary or meta-heuristic techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. T...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2012
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/17591/1/Overview%20of%20PSO%20for%20Optimizing%20Process%20Parameters%20of%20Machining%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/17591/ http://www.sciencedirect.com/science/article/pii/S1877705812000744 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In the current trends of optimizing machining process parameters, various evolutionary or meta-heuristic techniques
such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony
Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of
PSO techniques to optimize machining process parameter of both traditional and modern machining from 2007 to
2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered
by researchers in order to minimize or maximize machining performances. From the review, the most machining
process considered in PSO was multi-pass turning while the most considered machining performance was production
costs. |
|---|