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One of my primary research interests is to experimental analysis and predictive modeling of nanocrystalline microstructures in metallic materials undergoing cutting and other severe plastic deformation (SPD) processes, such as cold rolling and laser shock peening. To quantitatively predict the microstructural evolution in workpiece materials, I have developed a dislocation density-based
numerical framework to simulate the change of grain size, grain misorientation and material strengthening mechanisms during cutting and other SPD processes. The models developed in these studies are among the first quantitative predictive models in their relevant fields and agree well with experimental measurements. These models can effectively help study, design and optimize the relevant materials processing and other manufacturing processes, which include, but are not limited to, large strain cutting, hard machining, LAM, multi-pass cold rolling, laser shock peening and other SPD processes. For the excellence of this work, I received the ASME Best Paper Award at the 6th ASME 2011 International Manufacturing Science and Engineering Conference (MSEC2011) in Corvallis, Oregon, in June 2011 for the paper entitled "Dislocation density-based grain refinement modeling of orthogonal cutting of commercially pure titanium". It was selected out of more than 180 papers in the manufacturing area.
My current research project is geared toward modeling phase transformation for machining steels using a metallo-thermo-mechanical coupled material model. I have developed a numerical framework to model phase transformation, heat transfer and plastic deformation in the cutting of steels, which solves for cutting temperature, phase composition, chip morphology, and cutting force simultaneously by considering the thermo-mechanical properties and constitutive models of constituent phases such as ferrite, pearlite, austenite and martensite. The model will be a useful numerical tool in analyzing surface integrity issues like residual stress and white layer that are developed in hard machining, thermally enhanced machining and high speed machining.
My Ph.D. research is concerned with the experimental investigation and numerical modeling of laser-assisted machining (LAM) and micromachining processes of difficult-to-machine materials. LAM is a viable industrial option for precision machining of unmachinable ceramics and difficult-to-machine metal alloys. As an innovative alternative, LAM can replace grinding in semi-finishing and finishing processes, which greatly reduces the cost, consumes less energy, and eliminates the need for coolant. At Purdue University, I have conducted many projects for industries to help them design, setup, and optimize LAM processes for a variety of difficult-to-machine materials. Precise temperature control is the key for LAM. To achieve this goal, I have developed 3D finite volume heat transfer models using FORTRAN for various LAM processes such as facing, profile turning, and boring, to accurately predict the temperature field in the workpiece during the operation.
To analyze the surface defects caused by the size effect in micromachining using conventional micro tools, I have developed a novel finite element model with a strain gradient plasticity analysis to simulate the continuous chip formation for a complete micromilling cycle using Abaqus Explicit. My model was able to predict the steady-state tool and workpiece cutting temperatures in micromilling and laser-assisted micro milling. It was shown to be a useful tool in predicting size effect, tool wear, and surface integrity issues in micromachining.