用神经网络预测皮肤粘合剂剥离力，大幅减少仿真耗时。
Neural networks for the prediction of peel force for skin adhesive interface using FEM simulation
- 构建神经网络模型，基于有限元仿真数据预测剥离力最小值。
- 测试集 MSE 为 3.66×10⁻⁷，R² 达 0.94，精度高。
- 适合生物粘合材料设计与皮肤贴片优化的快速仿真需求。
研究皮肤粘合剂的剥离行为对医学粘合剂和透皮贴片等生物医学应用至关重要。传统实验测试和有限元法（FEM）虽为金标准，但资源消耗大、计算成本高，尤其在广泛材料参数空间分析时更为明显。本文提出一种基于神经网络的方法，用于预测粘合剂从皮肤分离所需的最小剥离力（F_min），减少重复FEM仿真需求，显著降低计算成本。基于90度剥离测试中不同粘合剂与断裂力学参数生成的仿真数据集，模型经五折交叉验证，最终架构可准确预测多种皮肤-粘合剂剥离行为，测试集均方误差（MSE）为3.66×10⁻⁷，决定系数（R²）达0.94，表现稳健。该方法实现了高效、可靠的粘合行为预测，大幅缩短仿真时间，同时保持高精度。机器学习与高保真生物力学仿真的融合，为皮肤粘合系统的设计优化提供可扩展框架，推动计算皮肤力学与生物粘合材料研究发展。
原文摘要 · Abstract (English)
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.
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