Teaching Interests: materials courses, mechanical courses, etc.
Despite the increasingly powerful artificial intelligence (AI) and data-driven industrial revolution, the analytical philosophies of science established about 400 years ago still undoubtedly hog academic research. In the long history of human eras, various materials and manufacturing processes play a core role. Emerging additive manufacturing (AM) provides a green and sustainable manufacturing approach in an inverse philosophy compared to traditional procedures, benefiting the current global decarbonization strategy. However, AM still needs to address many challenges due to its multi-physical processes in various materials systems or multiple situations that need to be applied before implementation in more fields and replacing more places of traditional manufacturing. Specifically, the primary aim of this investigation is to study the microstructural changes that affect material properties, such as elastic modulus and Poisson's ratio, and then how to search for the optimized processing parameters. These changes affect the material's performance, including residual stress, fractures, etc. To achieve this, the characterization of the microstructure of materials, mainly the surface/textures, grain size, and defects, if necessary, is of great importance. The texture and grain size simulation for multi-phase materials systems based on accurate physical stimuli modeling of processing is conducted. The influence of microstructural evolution on material properties such as elastic modulus strength is measured. Several paradigms are constructed to optimize manufacturing processes, utilizing combined advantageous analytical and data/machine learning or semi-analytical frameworks. In practically all contemporary industries, texture is essential. Because part geometry is vital in the real sector, this study first suggested a physics-based model to predict the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions. This function yields the temperature distribution, which serves as the single-phase crystallographic texturing model's information source. We predict and validate the orientations of materials in this model using three Euler Angles, the concepts of CET and thermodynamics, and Bunge computation. Grain size is a crucial microstructure characteristic directly related to strength qualities. It is often quantitatively represented by mean grain diameter. Creating an accurate physics-based analytical model for process-structure-property prediction still needs to be possible. The authors of this work first create the thermal model while taking the geometry of the molten pool and heat transmission boundary conditions into account. Next, the heating and cooling processes are simulated for the grain size, considering JMAK, thermal stress consideration, and grain refining. The influence of defects on the material properties, texture, and grain size is also considered. Specifically, thermal dynamics, Bunge calculation, and the CET model initially simulate the texture. The visco-plastic self-consistency model gains the properties of the impacted materials after determining the texture distribution. Afterward, AI is utilized to search for the optimized processing parameters or predict material properties so that this work could offer better instruction to real industries. Experimental results are used to validate the robustness of the models. This study bridges the gap between micro and macrostructures and the properties of materials in AM, potentially revolutionizing the industry and inspiring a new philosophy for science.