Abstract
In this paper, multi-objective optimization of hard turning has been reported. A neural network model was developed in order to model the surface roughness and tool wear characteristics of hard turning when CBN tools are used. Objective is to obtain optimum process parameters, which satisfies given limit, tool wear and surface roughness values and maximizes the productivity at the same time. A recently developed optimization algorithm called particle swarm optimization is used to find optimum process parameters. Accordingly, the results indicate that a system where neural network is used to model and predict process outputs and particle swarm optimization is used to obtain optimum process parameters can be successfully applied to multi-objective optimization of hard turning.
| Original language | American English |
|---|---|
| Pages (from-to) | 179-186 |
| Number of pages | 8 |
| Journal | Transactions of the North American Manufacturing Research Institute of SME |
| Volume | 33 |
| State | Published - 2005 |
| Event | North American Manufacturing Research Conference, NAMRC 33 - New York, NY, United States Duration: May 24 2005 → May 27 2005 |
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering
Keywords
- Hard turning
- Neural networks
- Optimization
- Swarm intelligence