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

该奖项将侧重于通过利用基于反馈的自主性的进步以及对材料和过程物理的基本理解来提高制造生态系统的效率和弹性。当前的制造范式以顺序和分离的方式处理材料加工阶段（即从原料中创造原料）和实际制造阶段（即使用原料创造最终产品）。这种顺序的观点导致缺乏系统级的理解，进而对效率（生产率和产品质量）和弹性（对材料不确定性和过程干扰）产生不利影响。该项目通过创建一个互动和集成的制造生态系统范例来解决这一挑战，该范例具有扩展的系统级视图，并借助于材料科学、制造科学和控制科学这三个学科的多学科融合。该研究通过开发计算模型来理解材料与制造的相互作用，以及自动化算法来实现高效和有弹性的制造，从而推进了制造科学，加强了美国制造业生态系统。这项研究将辅以对本科生和研究生的培训，特别注重代表性不足的群体、多学科教育材料的编写、以及为更广泛传播目的而开设的教程和讲习班。本研究的目标是发展对材料加工和实际制造耦合性质的理解，并利用这种理解来实现集成制造生态系统的自动化框架。研究目标是：(i)通过实验驱动的活动量化原料对原料特性的影响，（ii）通过数据驱动的分层建模框架了解具有基本非线性的过程物理，以及（iii）开发耦合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)如何结合原料及其比例的知识来预测原料的流变学和物理性质？（ii）原料性能和制造动力学之间的非线性相互作用如何影响复合材料性能？（iii）如何制定可接受的计算需求和足够的物理洞察力的降阶过程模型？（iv）如何系统地结合流变学和过程物理知识以及多模态数据流来创建一个自动化框架，最终提高材料加工中的原料质量和制造环境的稳健性？虽然这种框架的有效性将通过使用实验室规模的基于挤压的增材制造系统来评估，但预计该框架也可以广泛应用于任何制造系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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 the 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.