Machine learning-based optimization workflow of the homogeneity of
  spunbond nonwovens with human validation
Viny Saajan Victor Andre Schmeißer Heike Leitte Simone Gramsch
In the last ten years, the average annual growth rate of nonwoven production
was 4%. In 2020 and 2021, nonwoven production has increased even further due to
the huge demand for nonwoven products needed for protective clothing such as
FFP2 masks to combat the COVID19 pandemic. Optimizing the production process is
still a challenge due to its high nonlinearity. In this paper, we present a
machine learning-based optimization workflow aimed at improving the homogeneity
of spunbond nonwovens. The optimization workflow is based on a mathematical
model that simulates the microstructures of nonwovens. Based on trainingy data
coming from this simulator, different machine learning algorithms are trained
in order to find a surrogate model for the time-consuming simulator. Human
validation is employed to verify the outputs of machine learning algorithms by
assessing the aesthetics of the nonwovens. We include scientific and expert
knowledge into the training data to reduce the computational costs involved in
the optimization process. We demonstrate the necessity and effectiveness of our
workflow in optimizing the homogeneity of nonwovens.