Optimization of the operational parameters in a fast axial flow CW CO2 laser using artificial neural networks and genetic algorithms

• Published in: Optics and laser technology Vol. 40; no. 8; pp. 1000 - 1007
• Main Authors: Adineh, V.R., Aghanajafi, C., Dehghan, G.H., Jelvani, S.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Science 01.11.2008
• Subjects: Computers in experimental physics; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Gas lasers including excimer and metal-vapor lasers; Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Lasers; Neural networks, fuzzy logic, artificial intelligence; Optics; Physics; Electrical pumping; Neural networks; Genetic algorithm; Electric discharges; Optimization method; Artificial neural networks; Experimental study; Gas lasers; Fast-axial-flow CW CO2 laser; Axial flow; Genetic algorithms; Carbon dioxide lasers
• ISSN: 0030-3992
• DOI: 10.1016/j.optlastec.2008.03.003

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.