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

该奖项资助的研究将侧重于通过利用基于反馈的自主性以及对材料和工艺物理的基本理解来提高制造生态系统的效率和弹性。当前的制造范例将材料处理阶段（即，由原料产生原料）和实际的制造阶段（即，使用原料以产生最终产品）。这种顺序观点导致缺乏系统层面的理解，这反过来又对效率（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交互式和集成的制造生态系统范式来应对这一挑战，该范式具有更广泛的系统级视图-由三个学科的多学科融合提供帮助：材料科学，制造科学和控制科学。该研究通过开发计算模型来理解材料-制造相互作用，以及自动化算法来实现高效和弹性制造，从而推进制造科学并加强美国制造业生态系统。该研究将通过对本科生和研究生的培训来补充，特别关注代表性不足的群体，多学科教育材料的开发，本研究的目标是了解材料加工和实际制造的耦合性质，然后利用这种理解来实现面向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成分对原料性质的影响，（ii）通过数据驱动的分层建模框架理解具有基本非线性的过程物理，以及（iii）为耦合的材料制造生态系统开发最佳控制算法。在这个过程中，将回答以下基本问题：（i）如何联合收割机的原料及其比例的知识来预测原料的流变和物理性质？(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的性能？(iii)如何制定可接受的计算要求以及足够的物理见解的降阶过程模型？(iv)如何将流变学和工艺物理学知识与多模态数据流系统地联合收割机结合起来，以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？虽然该框架的有效性将通过实验室规模的基于挤出的增材制造系统进行评估，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NSF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。

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.