Collaborative Research: Make-it-Anywhere Distributed Additive Manufacturing: Enabling Part Quality Equivalence through Physics- and Data-Integrated Process Control

This award will establish the scientific foundation for a distributed additive manufacturing supply chain in which components can be produced on demand by geographically dispersed manufacturers with consistent quality and performance. Additive manufacturing offers unprecedented flexibility for producing complex components, reducing inventory requirements, shortening supply chains, and enabling rapid response to commercial, industrial, and national defense needs. However, parts manufactured at different facilities often exhibit substantial variations in defect severity, material structure, and functional performance due to differences in machines, materials, operating procedures, and environmental conditions. As a result, the qualification of new suppliers currently relies on extensive testing that requires significant time and cost. This research will establish a rigorous science-driven quantitative framework for achieving manufacturing equivalence across distributed production sites, thereby reducing barriers to supplier qualification and increasing the resilience of domestic manufacturing networks. The resulting capabilities will strengthen domestic manufacturing competitiveness, improve supply chain agility, and democratize access to advanced manufacturing technologies for small and medium-sized manufacturers. The project will integrate research outcomes into graduate and undergraduate education through hands-on course work, experiential learning, and industry-engaged training activities. The goal of this research is to discover the fundamental principles governing manufacturing equivalence in distributed additive manufacturing systems. The research will understand, predict, and mitigate the effect of variation in component design, machine characteristics, feedstock materials, processing conditions, and stochastic disturbances on defect formation, material structure evolution, and part performance. This research makes the following novel scientific contributions: (i) multiscale physics-based and data-enabled models to predict defect and material structure evolution subject to geographic variation in machines, materials, and methods; (ii) development of physics-informed federated learning approaches that quantify and transfer knowledge across manufacturing sites while accounting for design, machine, and material variability; and (iii) development of intelligent monitoring, optimization, and control methodologies that maintain equivalent manufacturing conditions and component quality across distributed suppliers. Fundamental questions addressed in this research include: (1) what factors govern defect formation and material structure evolution; (2) what information must be transferred to establish manufacturing equivalence; and (3) what process signatures indicate deviations from equivalent manufacturing conditions, and how and why such deviations affect statistical consistency. The research will advance novel process modeling, monitoring, and control methodologies imperative for mitigating variation affecting additive manufacturing, and thereby enable rapid, reliable domestic manufacturing of high-value components. 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.