CC BY 4.0Antweiler, DarioDarioAntweilerHarmening, MarcMarcHarmeningMarheineke, NicoleNicoleMarheinekeSchmeißer, AndreAndreSchmeißerWegener, RaimundRaimundWegenerWelke, PascalPascalWelke2022-12-072022-12-072022https://publica.fraunhofer.de/handle/publica/429664https://doi.org/10.24406/publica-60510.1016/j.simpa.2022.10042310.24406/publica-605Nonwoven 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.enNonwoven fiber materialManufacturingTextile fabricsMaterial property predictionGraph representationTensile strength behaviorjournal article