Machine learning framework to predict nonwoven material properties from fiber graph representations

Nonwoven fiber materials are omnipresent in diverse applications including insulation, clothing and filtering. Simulation of material properties from production parameters is an industry goal but a challenging task. We developed a machine learning based approach to predict the tensile strength of nonwovens from fiber lay-down settings via a regression model. Here we present an open source framework implementing the following two-step approach: First, a graph generation algorithm constructs stochastic graphs, that resemble the adhered fiber structure of the nonwovens, given a parameter space. Secondly, our regression model, learned from ODE-simulation results, predicts the tensile strength for unseen parameter combinations. •Framework to replace expensive numerical simulation via machine learning.•Surrogate model to simulate nonwoven fiber lay-down process.•Stochastic fiber graph generation within parameter space.•Regression model predicts tensile strength for unseen combinations.