基于机器学习的纺粘非织造布均匀性优化工作流及人工验证
Machine learning-based optimization workflow of the homogeneity of spunbond nonwovens with human validation
AI总结：
针对非织造布生产过程高度非线性、优化困难的问题，提出结合模拟数据、多机器学习代理模型与人工验证的均匀性优化工作流，融入专家知识降本，验证了其必要性与有效性。
摘要：
过去十年间，非织造布产量的年均增长率为4%。2020年与2021年，由于应对新冠疫情所需的FFP2口罩等防护用品对非织造布产品需求巨大，其产量进一步增长。由于生产过程的高度非线性，优化该过程仍是一项挑战。本文提出一种基于机器学习的优化工作流，旨在提升纺粘非织造布的均匀性。该优化工作流以模拟非织造布微观结构的数学模型为基础。基于该模拟器生成的训练数据，研究人员训练了多种不同的机器学习算法，以找到可替代耗时模拟器的代理模型。工作流采用人工验证方式，通过评估非织造布的外观美感来验证机器学习算法的输出结果。研究将科学知识与专家知识纳入训练数据，以降低优化过程涉及的计算成本。本文验证了所提工作流在优化非织造布均匀性方面的必要性与有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
