Smart manufacturing of Energy-Efficient Aerospace Structures

Who is working on this project: Leixin Ma

In this project, we aim at making two MAE machine operations, the drilling machine and milling machine smarter. The data and model systems focus on modeled systems with phenomena not easily modeled with first principles models, e.g. machining processes that affect part quality, for example, drilling and milling as well as the situational thermal and mechanical impacts on the precision of a part and energy and productivity efficiency of the processes. We will showcase AI/ML modeling in conjunction with first principles and parametric modeling to show how the DevOps provided by the SMIP (Smart manufacturing Innovation Platform) offers a new and more cost-effective platform for developing, engineering, testing, and sustaining hybrid, data-centric modeling. Meanwhile, we will also showcase control, performance and precision and energy-related improvement potential and demonstrate system-level modeling for identifying opportunities for the factory floor. Finally, by combining AI/ML and system-level modeling, we aim to search for strategies, such as the optimal spindle speed and federate that will maximize the energy efficiency of these Smart CNC Machining.

Funding: We are grateful for the financial support from the Clean Energy Smart Manufacturing Innovation Institute (CESMII) https://www.cesmii.org

Publication: TBD

GitHub: TBD

YouTube: TBD