Neural networks for the prediction of peel force for skin adhesive interface using FEM simulation

TL;DR

This paper introduces a neural network model trained on FEM simulation data to accurately and efficiently predict peel force in skin-adhesive interfaces, reducing the need for extensive computational simulations.

Contribution

The study presents a novel neural network approach that predicts peel force from FEM simulation data, significantly decreasing computational costs in skin-adhesive interface analysis.

Findings

Neural network achieved an MSE of 3.66*10^-7 and R^2 of 0.94.

Model accurately predicts peel force across various parameters.

Reduces simulation time while maintaining high accuracy.

Abstract

Studying the peeling behaviour of adhesives on skin is vital for advancing biomedical applications such as medical adhesives and transdermal patches. Traditional methods like experimental testing and finite element method (FEM), though considered gold standards, are resource-intensive, computationally expensive and time-consuming, particularly when analysing a wide material parameter space. In this study, we present a neural network-based approach to predict the minimum peel force (F_min) required for adhesive detachment from skin tissue, limiting the need for repeated FEM simulations and significantly reducing the computational cost. Leveraging a dataset generated from FEM simulations of 90 degree peel test with varying adhesive and fracture mechanics parameters, our neural network model achieved high accuracy, validated through rigorous 5-fold cross-validation. The final architecture was able to predict a wide variety of skin-adhesive peeling behaviour, exhibiting a mean squared error (MSE) of 3.66*10^-7 and a R^2 score of 0.94 on test set, demonstrating robust performance. This work introduces a reliable, computationally efficient method for predicting adhesive behaviour, significantly reducing simulation time while maintaining accuracy. This integration of machine learning with high-fidelity biomechanical simulations enables efficient design and optimization of skin-adhesive systems, providing a scalable framework for future research in computational dermato-mechanics and bio-adhesive material design.