Frank Liou

By Zhiqiang Fan and Frank Liou

Part of the book: Titanium Alloys

By Jacquelyn K. S. Nagel and Frank W. Liou

Part of the book: Manufacturing System

By Jingwei Zhang, Lei Yan, Wei Li and Frank Liou

In this chapter, a predictive multiscale model based on a cellular automaton (CA)-finite element (FE) method has been developed to simulate thermal history and microstructure evolution during metal solidification for direct metal deposition (DMD) process. The macroscopic FE calculation that is validated by the thermocouple experiment is developed to simulate the transient temperature field and cooling rate of single layer and multiple layers. In order to integrate the different scales, a CA-FE coupled model is developed to combine with thermal history and simulate grain growth. In the mesoscopic CA model, heterogeneous nucleation sites, grain growth orientation and rate, epitaxial growth, remelting of preexisting grains, metal addition, grain competitive growth and columnar to equiaxed phenomena are simulated. The CA model is able to show the entrapment of neighboring cells and the relationship between undercooling and the grain growth rate. The model predicts the grain size and morphological evolution during the solidification phase of the deposition process. The developed “decentered polygon” growth algorithm is appropriate for the nonuniform temperature field. Finally, the single- and multiple-layer DMD experiments are conducted to validate the characteristics of grain features in the simulation.

Part of the book: Additive Manufacturing of High-performance Metals and Alloys

By Aaron Flood and Frank Liou

Additive manufacturing with metal components is a complex, and currently cyclic, process due to the physical phenomena that are occurring. These phenomena can be mathematically modeled in order to predict the outcome of a specific aspect of the build. Coupling the mathematical models can then be used to develop a complete simulation, which can produce estimates for a range of characteristics for a part built using additive manufacturing techniques. This chapter will investigate the main models used in the simulation of metal AM. These models will include the modeling of thermal behavior, fluid dynamics, stress, and a selection of other auxiliary models, which are necessary to complete the simulations. For each of the models investigated, the various modeling techniques that have been developed will be presented along with their limitations, validation techniques, and parameters necessary to model the process correctly.

Part of the book: 3D Printing

By Heng Liu and Frank Liou

Direct metal deposition (DMD) has become very popular within the space of rapid manufacturing and repair. Its capability of producing fully dense metal parts with complex internal geometries, which could not be easily achieved by traditional manufacturing approaches, has been well demonstrated. However, the DMD process usually comes with high thermal gradients and high heating and cooling rates, leading to residual stresses and the associated deformation, which can have negative effect on product integrity. This paper studies the features of thermal stress and deformation involved in the DMD process by constructing a 3-D, sequentially coupled, thermomechanical, finite element model to predict both the thermal and mechanical behaviors of the DMD process of Stainless Steel 304 (SS 304). A set of experiments were then conducted to validate deformation using a laser displacement sensor. Comparisons between the simulated and experimental results show good agreement. This model can be used to predict the mechanical behavior of products fabricated by the DMD process and to help with the optimization of design and manufacturing parameters.

Part of the book: New Challenges in Residual Stress Measurements and Evaluation