Collaborative Research: Integrated Materials-Manufacturing-Controls Framework for Efficient and Resilient Manufacturing Systems

协作研究：高效、弹性制造系统的集成材料制造控制框架

• 批准号: 2346650
• 负责人: Satadru Dey
• 金额: $ 28.95万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2346650&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrated; Materials; Manufacturing

Research funded by this award will focus on enhancing the efficiency and resilience of manufacturing ecosystems by exploiting the advances in feedback-based autonomy and with fundamental understanding of materials and process physics. The current manufacturing paradigm treats the material processing stage (i.e., feedstock creation from raw ingredients) and the actual manufacturing stage (i.e., use feedstock to create final products) in a sequential and segregated manner. This sequential view results in a lack of system-level understanding which in turn adversely affects efficiency (production rate and product quality) and resilience (against material uncertainties and process disturbances). This project addresses this challenge by creating an interactive and integrated manufacturing ecosystem paradigm with a broadened system-level view – aided by multi-disciplinary convergence of three disciplines: material science, manufacturing science, and control science. The research advances the science of manufacturing and strengthens the U.S. manufacturing ecosystem by developing computational models to understand materials-manufacturing interactions, and automation algorithms to enable efficient and resilient manufacturing. The research will be complemented by training of undergraduate and graduate students with special focus on underrepresented groups, multi-disciplinary educational material development, and tutorials and workshops for broader dissemination purposes.The goal of this research is to develop an understanding of the coupled nature of materials processing and actual manufacturing – and then utilize this understanding to enable an automation framework towards integrated manufacturing ecosystem. The research objectives are: (i) quantification of the effects of raw ingredients on feedstock properties through an experimentally driven campaign, (ii) understanding of process physics with essential nonlinearities through a data-driven hierarchical modeling framework, and (iii) development of optimal control algorithms for coupled materials-manufacturing ecosystem. In the process, the following fundamental questions will be answered: (i) How to combine the knowledge of raw ingredients and their proportions to predict the rheological and physical properties of feedstock? (ii) How do the nonlinear interactions between feedstock properties and manufacturing dynamics impact the composite properties? (iii) How to formulate reduced order process models with acceptable computation requirements as well as enough physical insights? (iv) How to systematically combine knowledge of rheology and process physics and multi-modal data-stream to create an automation framework that ultimately enhances the feedstock quality in the material processing, and robustness of manufacturing environment? While the effectiveness of such a framework will be evaluated by using a laboratory-scale extrusion-based additive manufacturing system, it is anticipated that the framework can be broadly applied to any manufacturing systems as well.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.

该奖项资助的研究将着重于通过利用基于反馈的自主权的进步以及对材料和过程物理学的基本了解来提高制造生态系统的效率和韧性。当前的制造范式可以以顺序和隔离的方式处理材料处理阶段（即，从原始物质创建原料）和实际的制造阶段（即使用原料来创建最终产品）。这种连续的观点导致缺乏系统级别的理解，反过来会对效率（生产率和产品质量）和弹性（反对物质不确定性和过程障碍）产生不利影响。该项目通过创建具有扩展的系统级别的视图来解决这一挑战，这是在三个学科的多学科融合的帮助下：材料科学，制造科学和控制科学。这项研究通过开发计算模型来了解材料制造的相互作用以及自动化算法，以实现高效和依赖的制造，从而促进了美国制造生态系统的制造科学和优势。这项研究将通过对本科和研究生的培训完成，特别关注代表性不足的群体，多学科教育材料的发展以及教程和讲习班，以进行更广泛的传播目的。该研究的目的是建立对材料处理和实际制造本质的理解 - 然后利用该型号的建立型号，以建立自动化的构图。研究目标是：（i）通过实验驱动的运动来量化原始物质对原料属性的影响，（ii）通过数据驱动的层次结构建模框架了解具有基本非线性的过程物理学，（iii）开发辅助材料材料制造生态系统的最佳控制算法。在此过程中，将回答以下基本问题：（i）如何结合原始物质及其特性的知识，以预测原料的流变和物理特性？ （ii）原料性能与制造动力学之间的非线性相互作用如何影响复合性能？ （iii）如何制定具有可接受的计算要求以及足够的物理见解的减少订单过程模型？ （iv）如何​​系统地结合流变学和过程物理学的知识和多模式数据流以创建一个自动化框架，最终增强了材料处理中的原料质量以及制造环境的稳健性？虽然将通过使用基于实验室扩展的添加剂制造系统来评估此类框架的有效性，但可以预期该框架也可以广泛应用于任何制造系统。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估NSF的法定任务，并被认为是诚实的支持。