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Wavelength Decomposition of Hybrid Additive Manufacturing Power Signals and Their Relationship with Surface Integrity
Abstract
Additive Manufacturing (AM) has been a promising manufacturing technology in industrial applications and gained a massive amount of attention from researchers all over the world. Directed Energy Deposition (DED) is an AM process in which focused thermal energy is used to fuse materials by melting as they are deposited. One of the most significant challenges involved in current metal AM processes is improving repeatability and consistency. Substantial effort has already been invested in this area of research. In this dissertation, a milling tool not only played a role in subtractive manufacturing but also acted as a method of monitoring the properties of AM parts, such as surface integrity. Nonintrusive and inexpensive monitoring methods are always preferred for machining processes, and the use of milling power signals easily meets the requirements for simple process monitoring. Hybrid AM is the use of AM process with one or more secondary processes or energy sources that are fully coupled and synergistically affect part quality, functionality, and/or process performance. It is possible to detect the surface integrity of AM parts by analyzing milling power in Hybrid AM by milling features. The milling power was analyzed using the Data Dependent Systems (DDS) technique. DDS is an appropriate stochastic modeling and analysis approach for random systems characterization in the field of modal analysis. DDS can directly analyze the experimental data using time series modeling and removes the need to fully understand the system being analyzed. This dissertation presents research on three major topics related to DED and milling processes. First, the effects of tool wear on milling power signals in both time and frequency domains were investigated under various machining conditions. Next, the surface profiles and roughness were analyzed on DED 420 stainless steel samples at different locations. The nanohardness – milling power correlation and residual stresses – milling power correlation on DED samples were then established using the DDS approach.
Subject Area
Mechanical engineering|Industrial engineering
Recommended Citation
Wang, Xingtao, "Wavelength Decomposition of Hybrid Additive Manufacturing Power Signals and Their Relationship with Surface Integrity" (2020). ETD collection for University of Nebraska-Lincoln. AAI28086329.
https://digitalcommons.unl.edu/dissertations/AAI28086329