Framework to Accelerate Parameter Development for Laser Powder Bed Fusion

Metal Laser Powder Bed Fusion (M-LPBF) is a method of additive manufacturing that enables the fabrication of complex components that would not be possible through conventional manufacturing methods. M-LPBF is well suited for aerospace applications not only because of its ability to fabricate complex...

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Bibliographic Details
Main Author: Graybill, Benjamin C.
Other Authors: Hardt, David
Format: Thesis
Published: Massachusetts Institute of Technology 2023
Online Access:https://hdl.handle.net/1721.1/147520
Description
Summary:Metal Laser Powder Bed Fusion (M-LPBF) is a method of additive manufacturing that enables the fabrication of complex components that would not be possible through conventional manufacturing methods. M-LPBF is well suited for aerospace applications not only because of its ability to fabricate complex and efficient components, but it also can enable the reduction of cost and the schedule of programs. The recent advancements in material development could open the design space even further for aerospace applications, but the initial development process of evaluating a new material on a M-LPBF printer can be time consuming and costly. In this work, a framework to improve the efficiency and structure of M-LPBF process development is proposed. First, simulations of the melt pool were performed to understand the impact of primary process parameters on the dimensions of the melt pool. Then, tools to model the melt pool were tested and used in combination with analytical equations to identify an acceptable processing window for the M-LPBF process. Following this process parameter filtering, physical experiments were executed that investigated the impact of process and design parameters on various outputs connected to the melt pool, density, dimensional accuracy, and surface roughness of the coupons printed. Optimal parameter ranges can then be determined according to different design and process priorities. The framework developed in this project enables a material and machine agnostic approach to process parameter selection in less time and at a lower cost.