# Machine learning-based optimization workflow of the homogeneity of spunbond nonwovens with human validation

arXiv: https://arxiv.org/abs/2404.09604 · HTML: https://mlsanity.com/paper/2404.09604/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [Learning Physical Intuition of Block Towers by Example](https://mlsanity.com/paper/1603.01312/) (2016)
- [SPIGAN: Privileged Adversarial Learning from Simulation](https://mlsanity.com/paper/1810.03756/) (2018)
- [Simulation assisted machine learning](https://mlsanity.com/paper/1802.05688/) (2018)
- [Analysis of the fiber laydown quality in spunbond processes with simulation experiments evaluated by blocked neural networks](https://mlsanity.com/paper/1911.06213/) (2019)

## Abstract (arXiv)

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.