SUBJECT: Ph.D. Dissertation Defense
   
BY: Jinqiang Ning
   
TIME: Monday, October 21, 2019, 10:00 a.m.
   
PLACE: MRDC Building, 4404
   
TITLE: Analytical Modeling of Material Constitutive Behaviors and Process Mechanics in Precision Machining and Additive Manufacturing
   
COMMITTEE: Dr. Steven Y. Liang, Chair (ME)
Dr. Hamid Garmestani (MSE)
Dr. Thomas Kurfess (ME)
Dr. Shreyes N. Melkote (ME)
Dr. Ting Zhu (ME)
 

SUMMARY

Manufacturing processes, including precision machining and metal additive manufacturing, transform the raw materials into finished products with desired geometry and functionality. Physics-based analytical modeling methodology allows process planning and optimization because of the significant computational advantage without resorting to the finite element method or any iteration-based simulations. However, the analytical models are not readily available due to the complexity of those processes. In the study of precision machining, an inverse analysis methodology was employed with the mechanics model and gradient search method for the identification of material constitutive model parameters, namely the Johnson-Cook model constants. Analytical temperature models were developed based on the calculation of materials flow stress at the chip formation zone using constitutive model and mechanics model. The machining forces and machining temperatures were calculated in the machining ultra-fine-grained pure titanium, which was prepared by a severe plastic deformation method, namely equal channel angular extrusion. Ultra-fine-grained titanium was studied because of its increasing usefulness in biomedical and engineering applications. In the study of metal additive manufacturing, the temperature, thermal stress, thermal-stress induced distortion, residual stress, part distortion, and part porosity were calculated through analytical modeling based on thermal analysis and process mechanics. The exiting temperature models were significantly improved with considerations of scan strategy, boundary heat transfer, and powder material properties, which improved the predictive accuracy without significantly compromising the computational efficiency. The thermal stress and residual stress were calculated from thermal elasticity and elastoplastic relaxation procedure respectively. The thermal stress was employed in the calculation of in-situ thermal distortion. The residual stress was employed in the calculation of post-process part distortion, Furthermore, the part porosity due to lack-of-fusion was calculated from the areal thermal analysis, and powder bed porosity that calculated with statistical powder size distribution. Experimental validations are included with various materials for the validation of the presented models.