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

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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.