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Visual Parameter Space Analysis for Optimizing the Quality of Industrial Nonwovens.

Viny Saajan Victor, Andre Schmeiser, Heike Leitte

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    This study introduces a visual analytics framework to optimize nonwoven textile production. It uses machine learning and digital twins for real-time quality control and robust parameter settings in manufacturing.

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    Area of Science:

    • Materials Science and Engineering
    • Industrial Manufacturing
    • Data Science and Analytics

    Background:

    • Technical textiles, especially nonwovens used in medical masks, are critical but challenging to optimize.
    • Manufacturing process parameters for nonwovens significantly impact product quality.
    • Optimization of these parameters is difficult in live industrial settings.

    Purpose of the Study:

    • To present a visual analytics framework for interactive parameter space exploration and optimization in nonwoven production.
    • To integrate strategies for optimizing industrial nonwoven manufacturing processes.
    • To enable real-time interaction and rapid quality computations.

    Main Methods:

    • Survey of analysis strategies for optimizing industrial nonwoven production processes.
    • Augmentation of a digital twin with a machine learning surrogate model for fast quality predictions.
    • Integration of sensitivity analysis mechanisms for consistent product quality.

    Main Results:

    • Demonstrated interactive exploration of parameter spaces for nonwoven manufacturing.
    • Identified optimal parameter sets and investigated input-output relationships.
    • Conducted sensitivity analysis to determine robust quality settings.

    Conclusions:

    • The visual analytics framework effectively supports parameter optimization in nonwoven production.
    • The integration of machine learning and digital twins enables real-time quality control.
    • Sensitivity analysis ensures product quality consistency under varying parameters.