The research objective of this award is to establish an integrated physics-based, predictive modeling approach to improve surface integrity and optimize machining operations in the manufacturing of titanium and nickel-based alloyed end products. The goal is to control surface integrity, machining-induced layer thickness, depth-of-work hardening, tensile layer thickness, residual stresses, and micro-hardness of the end product, as well as representing the cutting tool parameter (material, coating and edge geometry) effects, and the effects of cutting conditions on these results. The proposed research will be conducted in a three-pronged approach, including physics-based modeling, experimental analyses and validation, and probabilistic-predictive modeling on (1) determination of detailed friction between tool and workpiece, and tool wear, (2) physics-based finite element simulations using temperature-dependent flow softening based constitutive material models to compute process outputs, including surface properties, and validating them with experiments, and (3) probabilistic predictive modeling and multi-criteria optimization.
If successful, the benefits and broader impacts of this research will be the use of predictive and physics-based simulation modeling approaches applied to machining-induced surface integrity predictions in titanium and nickel alloys. It is expected that this award will result in methods to improve surface integrity on machined titanium and nickel alloy metal components used in aerospace, medical devices and other related industries. This award will also provide exposure for graduate and undergraduate students to methods of predictive modeling and physics-based simulation modeling in manufacturing processes.