Hypothesis: In the traditional approach to Axisymmetric Drop Shape Analysis (ADSA), the determination of surface tension or interfacial tension is constrained by computational speed and image quality. By implementing a machine learning-based approach, particularly using a convolutional neural network (CNN), it is posited that analysis of pendant drop images can be both faster and more accurate.
Experiments: A CNN model was trained and used to predict the surface tension of drop images. The performance of our CNN model was compared to the traditional ADSA, i.e. direct numerical integration, in terms of precision, computational speed, and robustness in dealing with images of varying quality. Additionally, the ability of the CNN model to predict other drop properties such as Volume and Surface Area was evaluated.
Findings: Our CNN demonstrated a significant enhancement in experimental fit precision, predicting surface tension with an accuracy of (+/-) 1.22×10-1 mN/m and at a speed of 1.50 ms-1, outpacing the traditional method by more than 5×103 times. The model maintained an average surface tension error of 2.42×10-1 mN/m even for experimental images with challenges such as misalignment and poor focus. The CNN model also demonstrated showcased a high degree of accuracy in determining other drop properties.
Keywords: Axisymmetric drop shape analysis; Convolution neural network; Deep learning; Interfacial tension; Surface tension.
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