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Additive manufacturing (AM) simulations offer an alternative to expensive AM experiments to study the effects of processing conditions on granular microstructures. Existing AM simulations lack support from reliable validation techniques. The stochastic nature and spatial heterogeneity of microstructures make it difficult to validate the simulated microstructures against experimentally obtained images through statistical measures such as average grain size. Another challenge is the lack of reliable and automated methods to calibrate the model parameters, which are unknown and difficult to measure directly from experiments. To overcome these two challenges, we first present a novel metric to quantify the difference between granular microstructures. Then, using this metric in conjunction with Bayesian optimization, we present a framework that can be used to reliably and efficiently calibrate the model parameters. We employ this framework to first calibrate the substrate microstructure simulation and then the laser scan microstructure simulation for Inconel 625. Results show that the framework allows successful calibration of the model parameters in just a small number of simulations. [ABSTRACT FROM AUTHOR]

In this paper, a parallelized 3D cellular automaton computational model is developed to predict grain morphology for solidification of metal during the additive manufacturing process. Solidification phenomena are characterized by highly localized events, such as the nucleation and growth of multiple grains. As a result, parallelization requires careful treatment of load balancing between processors as well as interprocess communication in order to maintain a high parallel efficiency. We give a detailed summary of the formulation of the model, as well as a description of the communication strategies implemented to ensure parallel efficiency. Scaling tests on a representative problem with about half a billion cells demonstrate parallel efficiency of more than 80% on 8 processors and around 50% on 64; loss of efficiency is attributable to load imbalance due to near-surface grain nucleation in this test problem. The model is further demonstrated through an additive manufacturing simulation with resulting grain structures showing reasonable agreement with those observed in experiments. [ABSTRACT FROM AUTHOR]

Directed energy deposition (DED) is an advanced additive manufacturing technology for the fabrication of near-net-shape metal parts with complex geometries and high performance metrics. Studying the grain structure evolution during the process is pivotal to evaluating and tailoring the as-built products' mechanical properties. However, it is time-consuming to simulate the multi-layer deposition process using the physics-based numerical model to optimize the process parameters for achieving the desired microstructure. In this paper, a physics-informed machine learning algorithm to predict the grain structure in the DED process is proposed. To generate training data for the machine learning algorithm, we use an experimentally validated cellular automaton finite volume method (CAFVM) for DED Inconel 718, where CA is applied to model the grain structure and FVM to simulate the heat transfer. We develop a neural network model to identify the correlation between the local thermal features and their corresponding grain structure characteristics. The inputs and outputs of the neural network (NN) model are selected based on the governing physics, and a novel way to extract them is proposed. The NN model can quickly predict the grain structure characteristics with the local thermal data for thin-wall builds, and the predictions are in good agreement with the numerical simulation results. We expect the proposed method can benefit other metal additive manufacturing technologies to formulate efficient and accurate process-structure relationships and in-process feedback control. [Display omitted] • Predict additively manufactured grain structure with a machine learning method. • An 3D cellular automaton finite volume method for "the ground truth" microstructure. • Strategy to represent the grain size and aspect ratio variation in a neural network. • Simulated thin-wall builds of Inconel 718 by directed energy deposition. [ABSTRACT FROM AUTHOR]

Abstract Additive manufacturing (AM) processes produce unique microstructures compared with other manufacturing processes because of the large thermal gradient, high solidification rate and other local temperature variations caused by the repeated heating and melting. However, the effect of these thermal profiles on the microstructure is not thoroughly understood. In this work, a 3D cellular automaton method is coupled to a finite volume method to predict the grain structure of an alloy, e.g. Inconel 718, fabricated by AM. The heat convection due to thermocapillary flow inside the melt pool is resolved by the finite volume method for a real and accurate temperature field, while an enriched grain nucleation scheme is implemented to capture epitaxial grain growth following the mechanism identified from experiments. Simulated microstructure results are shown to be in qualitative agreement with experimental result and the effects of the process parameters on both thermal characteristics and the grain structure are identified. The 3D cellular automaton finite volume method results establish our approach as a powerful technique to model grain evolution for AM and to address the process-structure-property relationship. Graphical Abstract Unlabelled Image Highlights • A 3D cellular automata finite volume method for additive manufacturing is presented, including an enriched nucleation model • The grain structure in the Directed Energy Deposition of IN718 alloy is simulated by the proposed method • The effects of the processing parameters on the resulting grain structure of IN718 alloy are elaborated via the simulation • Sandwich- and zig-zag patterns of grain structure observed in experiments are captured and explained by the proposed model [ABSTRACT FROM AUTHOR]