Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence

TL;DR

This paper uncovers a correlation between the morphological evolution of splashing drops and the impact force exerted during impact, using an explainable AI approach to interpret features extracted from impact videos.

Contribution

It introduces a novel feature extraction method and interprets an XAI classifier to link drop morphology evolution with impact force, revealing physical-visual correlations.

Findings

Rate of feature contribution change matches impact force profile

Splashing features are most influential immediately after impact peak

XAI interpretation clarifies morphological and physical parameter relationships

Abstract

This study reveals a possible correlation between splashing morphology and the normalized impact force exerted by an impacting drop on a solid surface. This finding is obtained from a newly proposed feature extraction method and a subsequent interpretation of the classification of splashing and non-splashing drops performed by an explainable artificial intelligence (XAI) video classifier. Notably, the values of the weight matrix elements of the XAI that correspond to the extracted features are found to change with the temporal evolution of the drop morphology. We compute the rate of change of the contributions of each frame with respect to the classification value of a video as an important index to quantify the contributions of the extracted splashing and non-splashing features at different impact times to the classification of the XAI model. Remarkably, the rate computed for the extracted splashing features is found to closely match the profile of the normalized impact force, where the splashing features are most pronounced immediately after the normalized impact force reaches its peak value. This study has provided an example that clarifies the relationship between the complex morphological evolution of a splashing drop and physical parameters by interpreting the classification of an XAI video classifier.