Optimization of the operational parameters in a fast axial flow CW CO2 laser using artificial neural networks and genetic algorithms - Adineh, V.R.; Aghanajafi, C.; Dehghan, G.H.; Jelvani, S. (Optics and laser technology, 2008)
This paper presents an artificial intelligence approach for optimization of the operational parameters such as gas pressure ratio and discharge current in a fast-axial-flow CW CO2 laser by coupling artificial neural networks and genetic algorithm. First, a series of experiments were used as the learning data for artificial neural networks. The best-trained network was connected to genetic algorithm as a fitness function to find the optimum parameters. After the optimization, the calculated laser power increases by 33% and the measured value increases by 21% in an experiment as compared to a non-optimized case.