Основные понятия
A machine learning-based workflow is presented to efficiently optimize the homogeneity of spunbond nonwovens by leveraging simulation models and incorporating human validation.
Аннотация
The authors propose a machine learning-based workflow to optimize the homogeneity of spunbond nonwovens. The workflow combines numerical simulation models and data-driven machine learning techniques, while also incorporating human validation.
Key highlights:
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Parameter Selection:
- Six key process parameters that influence the quality of spunbond nonwovens are identified, including standard deviations, noise amplitude, speed ratios, and discretization step size.
- The quality of the nonwoven is measured using the coefficient of variation (CV) at multiple grid resolutions.
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Data Collection with Knowledge Integration:
- The size of the simulation sample is carefully chosen to balance computational cost and statistical uncertainty.
- The influence of discretization step size on product quality is analyzed and found to be negligible.
- Expert knowledge is used to determine optimal parameter ranges, and Latin hypercube sampling is employed to collect unbiased data.
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Model Selection and Evaluation:
- Multiple regression algorithms, including linear regression, support vector regression, polynomial regression, Bayesian regression, random forests, and artificial neural networks, are assessed.
- The artificial neural network model is found to provide the best accuracy and computational performance as a surrogate for the simulation tool.
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Homogeneity Optimization with Human Validation:
- A visualization tool is developed based on the trained neural network model to aid textile engineers in exploring the parameter space and identifying optimal settings.
- The simulated nonwoven images corresponding to the optimal parameter settings are validated by human experts based on aesthetic criteria.
The proposed workflow significantly reduces the time required for optimization compared to direct use of the computationally expensive simulation tool, while also incorporating human expertise to ensure the final product meets the desired quality standards.
Статистика
The coefficient of variation (CV) is used as the key metric to measure the homogeneity of the spunbond nonwovens. CV values are computed at seven different grid resolutions (0.5 mm, 1 mm, 2 mm, 5 mm, 10 mm, 20 mm, 50 mm).
Цитаты
"The goal of this work is therefore to combine simulation models with data-driven machine learning models along with human validation to improve the optimization of nonwoven production processes."
"We designed a machine learning model to accelerate the mapping of process parameters to nonwoven quality. To address the issue of missing training data, we employed simulation tools."