• Journal of Powder Materials
• /
• v.28 no.5
• /
• pp.417-422
• /
• 2021

In the present work, Inconel 718 alloy is additively manufactured on the Ti-6Al-4V alloy, and a functionally graded material is built between Inconel 718 and Ti-6Al-4V alloys. The vanadium interlayer is applied to prevent the formation of detrimental intermetallic compounds between Ti-6Al-4V and Inconel 718 by direct joining. The additive manufacturing of Inconel 718 alloy is performed by changing the laser power and scan speed. The microstructures of the joint interface are characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and micro X-ray diffraction. Additive manufacturing is successfully performed by changing the energy input. The micro Vickers hardness of the additive manufactured Inconel 718 dramatically increased owing to the presence of the Cr-oxide phase, which is formed by the difference in energy input.

• Journal of Powder Materials
• /
• v.31 no.3
• /
• pp.213-219
• /
• 2024

Additive Manufacturing (AM) is a process that fabricates products by manufacturing materials according to a three-dimensional model. It has recently gained attention due to its environmental advantages, including reduced energy consumption and high material utilization rates. However, controlling defects such as melting issues and residual stress, which can occur during metal additive manufacturing, poses a challenge. The trial-and-error verification of these defects is both time-consuming and costly. Consequently, efforts have been made to develop phenomenological models that understand the influence of process variables on defects, and mechanical/ electrical/thermal properties of geometrically complex products. This paper introduces modeling techniques that can simulate the powder additive manufacturing process. The focus is on representative metal additive manufacturing processes such as Powder Bed Fusion (PBF), Direct Energy Deposition (DED), and Binder Jetting (BJ) method. To calculate thermal-stress history and the resulting deformations, modeling techniques based on Finite Element Method (FEM) are generally utilized. For simulating the movements and packing behavior of powders during powder classification, modeling techniques based on Discrete Element Method (DEM) are employed. Additionally, to simulate sintering and microstructural changes, techniques such as Monte Carlo (MC), Molecular Dynamics (MD), and Phase Field Modeling (PFM) are predominantly used.

• Journal of Drive and Control
• /
• v.16 no.3
• /
• pp.42-50
• /
• 2019

3D printing AM processes have advantages in complex shapes, customized fabrication and prototype development stage. However, due to various parameters based on both the machine and the material, the AM process can produce finished output after several trials and errors in the initial stage. As such, minimizing or optimizing negative factors for various parameters of the 3D printing AM process could be a solution to reduce the trial-and-error failures in the early stages of such an AM process. In addition, this can be largely solved through software simulation in the preprocessing process of 3D printing AM process. Therefore, the objective of this study was to investigate a simulation technology for the AM software, especially Ansys Inc. The metal 3D printing AM process, the AM process simulation software, and the AM process simulation processor were examined. Through this study, it will be helpful to understand 3D printing AM process and AM process simulation processor.

• Journal of Drive and Control
• /
• v.16 no.3
• /
• pp.51-58
• /
• 2019

The objective of this study was to investigate a simulation technology for the AM field based on ANSYS Inc.. The introduction of metal 3D printing AM process, and the examining of the present status of AM process simulation software, and the AM process simulation processor were done in the previous study (part 1). This present study (part 2) examined the use of the AM process simulation processor, presented in Part 1, through direct execution of Topology Optimization, Ansys Workbench, Additive Print and Additive Science. Topology Optimization can optimize additive geometry to reduce mass while maintaining strength for AM products. This can reduce the amount of material required for additive and significantly reduce additive build time. Ansys Workbench and Additive Print simulate the build process in the AM process and optimize various process variables (printing parameters and supporter composition), which will enable the AM to predict the problems that may occur during the build process, and can also be used to predict and correct deformations in geometry. Additive Science can simulate the material to find the material characteristic before the AM process simulation or build-up. This can be done by combining specimen preparation, measurement, and simulation for material measurements to find the exact material characteristics. This study will enable the understanding of the general process of AM simulation more easily. Furthermore, it will be of great help to a reader who wants to experience and appreciate AM simulation for the first time.