Abstract

In this paper, a fast simulation of grain growth during directed energy deposition is presented. Controlling the microstructure is indeed essential to obtain the desired macroscopic behavior. We present a fast macroscopic simulation of temperature accounting for grain growth. The proposed approach relies on the coupling of recent contributions presenting: (i) a simulation of temperature in DED, (ii) a mesoscopic model of grain growth model based on Orientated Tessellation Updating Method, and (iii) a macroscopic stochastic model of grain growth. The general strategy is to compute the temperature field as a function of time during the entire process. The initial crystallization is not addressed in this contribution, and an arbitrary initial microstructure are introduced to test the model. The stochastic evolution of the grain structure due to thermal cycling is computed, and the final grain structure statistics is obtained in the entire part. The proposed model is sufficiently fast to enable simulations of large parts and parametric studies or optimization loops can be performed to adjust process parameters.

Abstract

Additive Manufacturing (AM) processes, such as Directed Energy Deposition (DED), offer great potential for producing complex and customized components. To optimize these processes, accurate simulations and numerical modeling techniques are essential. This paper presents a study on the thermal and mechanical calibration of DED AM process simulations on a part-scale. The research aims to develop a comprehensive finite element model that incorporates the multi-physics nature of the DED process, accurately predicting thermal behavior, internal stresses, and distortion of manufactured components. The calibration process involves experimental measurements and simulations using Abaqus software. The thermal calibration involves calibrating parameters such as emissivity, absorptivity, and convection coefficients, while the mechanical calibration focuses on plastic strain properties. Additionally, the study explores the simulation of multi-material prints and functionally graded materials. The results demonstrate that the models can accurately represent thermal and mechanical phenomena, with calibration of material properties playing a crucial role. The paper concludes with recommendations for further validation, including demonstrator prints and investigations into simulation parameters. This research contributes to advancing the understanding and application of DED AM simulations, enabling more accurate and reliable predictions for industrial applications.

Abstract

Optimal material properties of duplex stainless steels generally require near 50-50 ferrite-austenite microstructures. The development of additive manufacturing of duplex steels is hindered by difficulty in controlling cooling conditions to ensure a balanced phase ratio. In addition, non-uniform phase distribution is usually observed. Thus, sufficiently fast part scale process simulations are interesting to optimize process parameters to better predict and control the temperature history during fabrication and therefore solid-state phase transitions. Furthermore, stresses should also be taken into account in the optimization of the phase field in order to avoid cracking, buckling or excessive distortions. Numerical results obtained from a fast modeling of directed energy deposition including thermal analysis, diffusion of alloying element to account for phase transitions, and stress computation are analyzed. On this basis, we investigate the effect on stresses of an optimized fabrication strategy designed to target uniform and balanced ferrite-austenite ratio with respect to a reference printing strategy