Parameter Optimization for NC Machine Tool Based on Golden Section Search Driven PSO

We have proposed a modified PSO; GPSO (golden-section-search driven particle swarm optimization) which updates only one particle in a generation based on a strategy: golden section search and steepest descent method. It was proved to be effect in various optimization problem. In this paper, first, this GPSO is revised to make clear its effectiveness. Then, the GPSO is utilized to optimize control parameters in NC machine tools. Parameters which are said to be difficult to optimize in a NC machine tool, is chosen and the roles of those parameters arWe have proposed a modified PSO[1]; GPSO (golden-section-search driven particle swarm optimization) which updates only one particle in a generation based on a strategy: golden section search and steepest descent method. It was proved to be effect in various optimization problem. In this paper, first, this GPSO is revised to make clear its effectiveness. Then, the GPSO is utilized to optimize control parameters in NC machine tools. Parameters which are said to be difficult to optimize in a NC machine tool, is chosen and the roles of those parameters are scrutinized. Based on those scrutiny, fitness are defined for parameters. In order to verify optimization performance of the algorithms (GA, PSO, GPSO), a hardware-in-the-loop system with a NC machine tool is set up and on-line optimization experiments are conducted using the system. In experiments, the GPSO shows better optimization performance.e scrutinized. Based on those scrutiny, fitness are defined for parameters. In order to verify optimization performance of the algorithms (GA, PSO, GPSO), a hardware-in-the-loop system with a NC machine tool is set up and on-line optimization experiments are conducted using the system. In experiments, the GPSO shows better optimization performance.