April 12
@
11:00 AM
–
12:00 PM
Manufacturing processes, such as stamping, forging, and rolling rely heavily on process simulation and in-situ correction to produce high volume of complex parts. Incremental sheet forming and additive manufacturing (AM) processes are used to make small volume of specialty products. AM processes rely heavily on CAD models for process design and closed loop control for production quality. In comparison, a manufacturing system designed for autonomous manufacturing of custom parts will require considerable automation using ML, DL, and AI models to optimize forming processes, and production paths. Calibration of these models heavily rely on data generated from physics-based forming and material models. To the best of our knowledge, a fully autonomous manufacturing system does not currently exist. However, the author and several co-investigators from multiple universities and national labs are working on projects with the goal to develop the framework for hybrid autonomous manufacturing systems for metals and polymer composites. In this presentation, the author will present some results from physics-based models developed to predict the evolution of the microstructure during thermo-mechanical processing of metals and reinforced polymer composites. The results from these models have been used to develop surrogate deep learning (DL) models that accurately predict the evolution of mechanical properties of materials during the manufacturing processes.
Bio: Farhang Pourboghrat
BSME and MSME – University of Iowa (1981 and 1983, respectively)
PhD in Mechanical Engineering – University of Minnesota (1990).
1990 to 1998 – Staff Scientist at the Alcoa Technical Center.
1998 to 2015 – Faculty in the ME Department at Michigan State University.
January to June 2005 – sabbatical leave at Rice University.
2015 to 2017 – Professor in the Integrated Systems Engineering Department at The Ohio State University.
Also holds a joint appointment in MAE Department at OSU.
2017 to present – Professor and Chair of the Integrated Systems Engineering at OSU.
Research Interests – Multiscale characterization of materials and microstructure-based modeling of forming processes, including additive manufacturing, sheet metal forming, tube hydroforming, incremental sheet forming, and thermoforming of fiber-reinforced thermoplastic composites.
Current Research – Applying machine learning (ML) to corollate microstructural properties of metals and fiber-reinforced polymer composites with their anisotropic properties.