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The recording of atmospheric pressure plasmas (APP) electro-acoustic emission data has been developed as a plasma metrology tool in the last couple of years. The industrial applications include automotive and aerospace industry for surface activation of polymers prior to bonding [1, 2, and 3]. It has been shown that as the APP jets proceeds over a treatment surface, at a various fixed heights, two contrasting acoustic signatures are produced which correspond to two very different plasma-surface entropy states (blow arc ∼ 1700 ± 100 K; and; afterglow ∼ 300-400 K) [4]. The metrology challenge is now to capture deterministic data points within data clusters. For this to be achieved new real-time data cluster measurement techniques needs to be developed [5]. The cluster information must be extracted within the allotted process time period if real-time process control is to be achieved. This abstract describes a theoretical structural complexity analysis (in terms crossing points) of 2 and 3-dimentional line-graphs that contain time-series data. In addition LabVIEW implementation of the 3-dimensional data analysis is performed. It is also shown the cluster analysis technique can be transfer to other (non-acoustic) datasets. [ABSTRACT FROM AUTHOR]

Low-order moment around the mean (mean, standard deviation and skewness) analysis of the time evolving acoustic emission of an air atmospheric pressure plasma jet is performed as a function of nozzle-to-surface gap (7 to 0.5 cm), drive frequency (19 and 25 kHz) and air flow rate (35.7 and 76.6 l/m). The shapes of the probability distribution of each measurement dataset (containing 433 samples) are found to exhibit deterministic correlations with contrasting entropy process regions. The results indicate that the heated air is channeled along the surface and has a preferred backscatter angular distribution. Separation between the blown arc process (gap = 0.5 cm and 1740 ±100 K) and the afterglow (gap = 1 to 7 cm and 300-400 K) is observed in the mean acoustic amplitude and standard deviation. In addition the blown arc process exhibits a skewness of +0.055 and the afterglow has skewness values from -0.05 to -0.4. These results illustrate how acoustic information can be used to differentiate plasma-surface entropy states. [ABSTRACT FROM AUTHOR]