S. K. Bhat - Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
G. D. Deepak - Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

Cold Atmospheric Pressure Plasma (CAP) is very potent and impactful technology implemented for both technological and biomedical applications. This paper focuses on the implementation of artificial neural network (ANN) for a novel double ring electrode based cold atmospheric pressure plasma which is to operated only in the glow discharge region for its application in biomedical field. ANN inherently helps in visualizing the effective output parameters such as peak discharge current, power consumed, jet length (with sleeve) and jet length (without sleeve) for given set of input parameters of supply voltage and supply frequency using machine learning model. The capability of the ANN model is demonstrated by predicting the output parameters of the CAP beyond the experimental range. Finally, the optimized settings of supply voltage and supply frequency will be determined using the composite desirability function approach to simultaneously maximize the peak discharge current, jet length (with sleeve) and jet length (without sleeve), and minimize the power consumption.

Artificial Neural Network, cold plasma, Machine Learning, Biomedical devices, Desirability function analysis, Ring electrode