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）产品是包括聚合物树脂，色素分散剂和其他添加剂在内的化学物质的复杂混合物。为了描述诸如色彩强度，耐用性和保质期之类的P/C品质，需要大量的消费者规格。这些消费者属性对P/C成分的属性和数量的依赖性和制备条件很复杂，了解不足，目前无法使用基于物理的数学模型进行预测。这与P/C成分本身形成鲜明对比，但是，其特性通常是可以很好地理解的，并且可以通过严格的化学反应和混合模型预测。预计该项目将开发一个能够预测这些复杂混合物的最终性质的模型。预计这将有助于产品设计以及实时质量预测，缺陷检测和诊断以及产品质量监测和控制。这项工作的预期经济影响是涂料/涂料（P/C）产品的更快设计和定制。 该研究计划旨在使用混合模拟方法来克服预测P/C最终产品质量的挑战，该方法将机器学习方法与基于物理的建模元素相结合。从本质上讲，该合作工业合作伙伴的数十年制造数据将用于发现制造过程条件与使用统计机器学习技术的P/C产品质量之间的关系之间的关系。这将以人工神经网络的形式创建一个黑框模型，该模型将作为输入基于物理的成分建模元素的预测，并将预测最终的P/C质量。这项研究将为内部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，并将启用方法来监视和控制P/C产品质量。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Drexel合作计划将这项研究系统地整合到本科教育中。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。

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
• 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