Thermal modeling of directed energy deposition additive manufacturing using graph theory

Abstract

Purpose The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach In this study, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts. Practical implications The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation. Originality/value This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

Distribution of temperature and residual stresses in GMA-DED based wire-arc additive manufacturing

“…The gas metal arc-based directed energy deposition (GMA-DED) process exhibits a very high deposition rate and negligible material waste compared to other forms of metal additive manufacturing (AM) processes (Alfattni, 2022; Lidong and Alexander, 2016; Riensche et al , 2023). However, it experiences high heat input during the deposition of layers and yields significant residual stress (RS) in the component, which can further cause the distortion of the substrate (Özdemir and Korkmaz, 2023; Williams et al , 2016).…”

Effect of Heat Treatment on Corrosion and Mechanical Properties of M789 Alloy Fabricated Using DED

“…Moreover, the excellent weldability of maraging steels due to their low carbon content makes them highly suitable for additive manufacturing (AM), where rapid cooling rates can induce a martensitic structure during fabrication. Laser-directed energy deposition (DED) is one of the most widely utilized AM techniques which offers multiple advantages over other traditional manufacturing techniques such as near-net-shape parts, a low buy-to-fly ratio, large scale Metals 2023, 13, 1214 2 of 11 printing and, most importantly, the repair of worn or damaged parts [10][11][12][13][14][15][16]. DED is a powder-based deposition process, where a powder and laser are simultaneously passed on the substrate.…”

Deep Learning for In-Situ Layer Quality Monitoring during Laser-Based Directed Energy Deposition (LB-DED) Additive Manufacturing Process

“…In addition to reduced cost, AM processes have the potential to become fully automated. Research has demonstrated a strong desire to develop fully automated metal AM techniques with rapid modeling capabilities to reduce the potential of defects and ensure quality standards of parts is achieved [6,7]. In [8], Panchagnula and Simhambhatla highlight the ability of the metal AM process to produce complex designs including sudden overhang features (features that are perpendicular to the deposition direction or nearly horizontal features) without support material.…”