Optimization of Performance Models for Interlayer Meltblown Nonwovens with Random Forest

In the context of rapid societal and technological advancements, there has been a pronounced elevation in public health awareness. Among various protective measures, meltblown nonwoven materials have emerged as an indispensable component in protective masks, offering a crucial respiratory barrier for users. This study endeavors to provide an in-depth examination of this pivotal material. By systematically analyzing extensive datasets of meltblown fabric manufacturing processes and taking into account key parameters such as receiving distance, air velocity, and material thickness, this paper, grounded on historical production parameters, harnesses advanced machine learning techniques to meticulously design and optimize the performance control model for interlayer melt-blown nonwoven materials. The central aim of this research is to further enhance the efficacy of this material in health protection applications.