- Sc.D., Mechanical Engineering, ETH Zurich, Switzerland, 2016
- Professional Engineer Mechanical, California, #41837
- Research Scientist, Massachusetts Institute of Technology (MIT), 2018 – 2020
- Postdoctoral Fellow, Massachusetts Institute of Technology (MIT), 2016 – 2018
- Lecturer, Mechanical Engineering, Massachusetts Institute of Technology (MIT), 2018
Dr. Gorji's expertise focuses on the theoretical, experimental, and numerical aspects of material forming under quasi-static and dynamic conditions. His area of expertise includes material characterization, failure and damage analysis, finite element modeling, and computational mechanics. Applications include sheet metal forming, impact and crashworthiness, additive manufacturing, lithium-ion batteries, and structural modeling of metallic stents as well as the polymer bioabsorbable vascular scaffolds. Dr. Gorji's recent interest is leveraging artificial neural network modeling for industrial applications, manufacturing processes, and engineering practices.
Through his research on aluminum alloy composites at ETH Zurich — in collaboration with Daimler AG, Novelis, AutoForm, and GOM mbH — Dr. Gorji is a recognized expert in the sheet metal forming community. His research encompassed developing new experimental techniques to calibrate a fracture model that predicts crack initiation in deep drawing operations. The models were then implemented in a finite element code for industrial applications. During his Ph.D., he also made significant advancements in the study of friction phenomena that occurs during extrusion processes by designing an environmentally dependent experiment.
During a postdoctoral appointment at MIT within Professor Wierzbicki's Impact and Crashworthiness Lab, Dr. Gorji was a member of the MIT Industrial Fracture Consortium, which is supported by the worldwide automotive and steel/aluminum industries. He was responsible for designing a new experimental technique, characterizing plasticity, and fracture prediction for many industrial applications, including weldments, cold and hot forming of steels and aluminum sheets. During his subsequent appointment as Research Scientist at MIT, Dr. Gorji demonstrated that neural networks provide a very powerful modeling framework suitable for data-driven constitutive modeling, such as capturing temperature and strain rate dependent behavior of metallic alloys and polypropylene materials. His research also highlighted the potential of machine learning algorithms to describe the elastoplastic response of lithium-ion batteries and composite structures.