CDS&E: GOALI: Paints/Coatings In-Silico Product Design and Real-Time Product-Quality Monitoring and Control

CDS

• 批准号: 1953176
• 负责人: Masoud Soroush
• 金额: $ 30.24万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953176&HistoricalAwards=false
• 关键词: CDS& GOALI; Paints; Coatings; Silico

Modern paint/coating (P/C) products are complex mixtures of chemicals that include polymer resins, pigment dispersants, and other additives. To describe P/C qualities such as color strength, durability, and shelf life, a vast set of consumer specifications are required. The dependence of these consumer attributes on the properties and amounts of the P/C ingredients and the preparation conditions is complex, poorly understood, and currently impossible to predict using physically based mathematical models. This is in contrast to the P/C ingredients themselves, however, whose properties generally are well-understood and can be predicted in advance by rigorous chemical reaction and mixing models. This project is expected to develop a model capable of predicting final properties of these complex mixtures. This is expected to aid in product design, and real-time quality prediction, defect detection and diagnosis, and product quality monitoring and control. The expected economic impact of this work is faster design and customization of paint/coating (P/C) products. This research program aims to overcome the challenges of predicting P/C final product qualities using a hybrid simulation approach that combines machine learning methods with physically-based modeling elements. At its core, decades of manufacturing data from the industrial partner of this collaboration will be used to uncover relationships between manufacturing processing conditions and the poorly understood P/C product qualities using a statistical machine learning technique. This will create a black-box model in the form of an artificial neural network which will take as input the predictions of the physically based ingredient modeling elements and will predict final P/C qualities. This research will produce robust computational methods for in-silico P/C product design, real-time P/C product quality prediction, product defect detection and diagnosis, and will enable methods to monitor and control P/C product quality. The computational methods can be applied directly or extended to other manufacturing processes. The team plans to integrate this research systematically into undergraduate education through the Drexel Co-op Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代油漆/涂料 (P/C) 产品是复杂的化学品混合物，其中包括聚合物树脂、颜料分散剂和其他添加剂。为了描述 P/C 的品质，例如颜色强度、耐用性和保质期，需要大量的消费者规格。这些消费者属性对 P/C 成分的性质和数量以及制备条件的依赖性很复杂，人们对此知之甚少，并且目前无法使用基于物理的数学模型进行预测。然而，这与 P/C 成分本身形成鲜明对比，P/C 成分本身的特性通常是众所周知的，并且可以通过严格的化学反应和混合模型提前预测。该项目预计将开发一个能够预测这些复杂混合物的最终特性的模型。预计这将有助于产品设计、实时质量预测、缺陷检测和诊断以及产品质量监控和控制。这项工作的预期经济影响是更快地设计和定制油漆/涂料 (P/C) 产品。 该研究项目旨在利用将机器学习方法与基于物理的建模元素相结合的混合仿真方法来克服预测 P/C 最终产品质量的挑战。其核心是，此次合作的工业合作伙伴数十年的制造数据将用于通过统计机器学习技术揭示制造加工条件与人们知之甚少的 P/C 产品质量之间的关系。这将以人工神经网络的形式创建一个黑盒模型，该模型将基于物理的成分建模元素的预测作为输入，并预测最终的 P/C 质量。这项研究将为计算机 P/C 产品设计、实时 P/C 产品质量预测、产品缺陷检测和诊断提供强大的计算方法，并将实现监测和控制 P/C 产品质量的方法。计算方法可以直接应用或扩展到其他制造过程。该团队计划通过德雷塞尔合作项目将这项研究系统地整合到本科教育中。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Data‐driven prediction and optimization of liquid wettability of an initiated chemical vapor deposition‐produced fluoropolymer

数据驱动的化学气相沉积生产的含氟聚合物的液体润湿性预测和优化

• DOI: 10.1002/aic.17674
• 发表时间: 2022-06
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Schwartz, Daniel;Nguyen, Tien;Chen, Zhengtao;Lau, Kenneth K.;Grady, Michael C.;Shokoufandeh, Ali;Soroush, Masoud
• 通讯作者: Soroush, Masoud