SUBJECT: Ph.D. Proposal Presentation
BY: Elham Mirkoohi
TIME: Wednesday, July 25, 2018, 1:00 p.m.
PLACE: MARC Building, 201
TITLE: Analytical Modeling of Residual Stress in Metal Additive Manufacturing
COMMITTEE: Dr. Steven Y. Liang, Chair (ME)
Dr. Hamid Garmestani (MSE)
Dr. Thomas Kurfess (ME)
Dr. Shreyes Melkote (ME)
Dr. Christopher Saldana (ME)


Additive Manufacturing (AM) comprises a family of different technologies that build up parts by adding materials layer by layer at a time based on a digital 3D solid model. After thirty years of development, AM has become a mainstream manufacturing process with more materials and new technologies involved in this process. Undoubtedly, the most dramatic and challenging development of group of technologies has been the printing of metals. Nowadays, the use of AM for the production of parts for final products continues to grow. Organizations around the world are successfully applying the technology to the production of finished goods. AM allows design optimization and produces customized parts on-demand with almost similar material properties with the conventional manufactured parts. It does not require the use of coolants, fixtures, cutting tools and other assisting resources. The advantages of AM over conventional manufacturing can change the world of industry and lead to a new industrial revolution.
The available knowledge and technology to-date on the description and prediction of metal AM process have been fragmented, mostly driven by phenomenological or numerical observations, and primarily limited to macroscopic analysis in nature, thus restricting the full capability potential of the AM process. The project will uniquely contribute to the successful upfront design and optimization of metal AM by virtue of the proposed computational mechanics of materials methodology, rapid physics-based explicit solution approach, and inverse computation capability. The goal of this research can be fragmented into two main categories. First, forward computation for process prediction and simulation. Second, backward computation for process design and optimization.
The process prediction contains the temperature prediction, the thermal stress prediction, and residual stress prediction during metal additive manufacturing. The backward computation is the proposal’s key capability to design the process parameters for specific temperature, thermal stress, and residual stress.