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.