Session: 02-01-02: 7th Annual Conference-Wide Symposium on Additive Manufacturing: Metals II

Paper Number: 99707

99707 - Modeling of Selective Laser Melting (Slm) Process 

Additive Manufacturing (AM) is rapidly advancing from a tool for concept modeling and rapid prototyping to a tool for production of functional parts in the aerospace, medical and automotive industries. The Metal Additive Manufacturing (MAM) process, also known as metal 3D printing, is a relatively new generation AM process capable of producing metallic components using Computer Aided Design (CAD) data. The MAM process is an important research area given that metals are used as a major raw material for many engineering applications. The ability to produce complex shaped components, light-weighting of parts, part consolidation, and quick design iterations are some benefits of the MAM process. The Selective Laser Melting (SLM) process is a type of AM Powder Bed Fusion (PBF) process in which a high intensity laser is used as the energy source to melt and fuse specific portions of metal powder layer. The SLM process is gaining relevance as a production method in the industry. The process permits the manufacture of metallic parts with complex geometries. Additionally, SLM parts possess mechanical properties that are often comparable to those of conventionally manufactured parts. However, defects such as keyholing, balling, and lack of fusion may significantly affect AM part quality and mechanical properties. The amount and type of defects generated in SLM process are influenced by the input laser energy. The energy input is related to the process parameters, including laser power, scan speed, hatch spacing, and powder layer thickness. The choice of process parameters that optimize the build process is a key consideration in SLM additive manufacturing.

In this study, the SLM process was modeled using a commercial computational package (ANSYS Additive Science). The goal was to establish optimal process parameters that would result in defect-free components and to also determine the impact of the laser power and scan speed on the grain size distribution. Ti6Al4V and AlSi10Mg alloys were used in this study. To validate the simulation results, ANSYS input process parameters were matched with published experimental data. Simulation results for the melt pool dimensions (width and depth) and grain sizes were found to be qualitatively consistent with the referenced experimental data. The quantitative melt pool width and depth, as well as grain sizes obtained from the simulation differed from the referenced experimental data. Further work will be done to enhance the quantitative results by calibrating and tweaking the simulation system configuration and settings to better match the SLM machine settings used in experimental work. The product-to-market time, production costs, and material consumption will all be significantly reduced by modeling the SLM process rather than using trial-and-error experimental approach to obtain optimum process parameters.

Presenting Author: ANNE MUNYASIA Tennessee state university

Presenting Author Biography: Anne Munyasia is an aspiring engineer passionate about STEM. Anne is currently a graduate research/teaching assistant in the Department of Mechanical and Manufacturing Engineering at Tennessee State University (TSU). She is pursuing a master’s degree program in mechanical engineering at TSU. <br/>Anne’s current research involves the modeling of Selective Laser Melting (SLM) additive manufacturing process to efficiently determine an operating window with process parameters that will result in a good build. She is also involved in the design of coaxial 3D printing nozzle for Direct Write Epoxy Resins that can be used to extrude two distinct epoxy resins. <br/>Her research interest include design and manufacturing, computational modelling, 3D printing and additive manufacturing, dynamics and control.

Authors:

ANNE MUNYASIA Tennessee state university
Ayodeji Fawole Tennessee State University
Abiodun Fasoro Tennessee State University
Lee Keel Tennessee State University