Prediction of static properties of LPBFAlSi10Mg samples post-treated by Friction Stir Processing or thermal treatments

peer reviewedLongLifeA

Belgium

This work focuses on the thermal modeling of the Directed Energy Deposition of a composite coating (316L stainless steel reinforced by Tungsten carbides) on a 316L substrate. The developed finite element model predicts the thermal history and the melt pool dimension evolution in the middle section of the clad during deposition. Numerical results were correlated with experimental analysis (light optical and scanning electron microscopies and thermocouple records) to validate the model and discuss the possible solidification mechanisms. It was proven that implementation of forced convection in the boundary conditions was of great importance to ensure equilibrium between input energy and heat losses. The maximum peak temperature shows a slight increase trend for the first few layers, followed by an apparent stabilization with increasing clad height. That demonstrates the high heat loss through boundaries. While in literature, most of the modeling studies are focused on single or few layer geometries, this work describes a multi-layered model able to predict the thermal field history during deposition and give consistent data about the new materiel. The model can be applied on other shapes under recalibration. The methodology of calibration is detailed as well as the sensitivity analysis to input parameters.Peer reviewe

Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations

peer reviewedIn this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accurate low dimensionality reduction of simulated microstructural images and to provide time-series analysis. The dataset was indeed constructed from high-fidelity Phase-Field simulations. Analyses demonstrated that the association of auto-encoder neural networks and principal component analyses leads to ensure efficient and significant dimensionality reduction: 1/196 of reduction ratio with more than 80% of accuracy. These findings give insight to apply analyses on data from the latent dimension. Application of Long Short Term Memory (LSTM) neural networks showed the possibility of making next frame predictions; that makes possible the acceleration of Phase-Field simulation without the need of high computing resources. We discussed the application of such a framework on various areas of research. Different methods are proposed from the conducted analyses, in order to ensure dimensionality reduction (auto-encoders, principal component analysis, Artificial Neural Networks) and time-series analysis (LSTM, Gated Recurrent Unit (GRU)).9. Industry, innovation and infrastructur

How finite element simulations and phase field method interact to predict material properties of additive manufacturing samples

explanation of the framework defined in our team to model LPBF (SLM) proces

Thermal field prediction in DED manufacturing process using Artificial Neural Network

peer reviewedIn the last decade, machine learning is increasingly attracting researchers in several scientific areas and, in particular, in the additive manufacturing field. Meanwhile, this technique remains as a black box technique for many researchers. Indeed, it allows obtaining novel insights to overcome the limitation of classical methods, such as the finite element method, and to take into account multi-physical complex phenomena occurring during the manufacturing process. This work presents a comprehensive study for implementing a machine learning technique (artificial neural network) to predict the thermal field evolution during the direct energy deposition of 316L stainless steel and tungsten carbides. The framework consists of a finite element thermal model and a neural network. The influence of the number of hidden layers and the number of nodes in each layer was also investigated. The results showed that an architecture based on 3 or 4 hidden layers and the rectified linear unit as the activation function lead to obtaining a high fidelity prediction with an accuracy exceeding 99%. The impact of the chosen architecture on the model accuracy and CPU usage was also highlighted. The proposed framework can be used to predict the thermal field when simulating multi-layer depositio

Presentation: Adaptive time stepping approach for Phase-F1ield modeling of phase separation and precipitates coarsening in additive manufacturing alloys

peer reviewedIn the present work, the capacity of phase field method to highlight microstructural changes during the spinodal decomposition of a given binary alloy basing on the Cahn-Hilliard equation is presented. Then, growth and coarsening of precipitates are studied using the KKS (Kim-Kim-Suzuki) model, which includes Cahn-Hilliard and Allen-Cahn equations. The implementation of time stepping algorithms to resolve Phase-Field equations is illustrated. Within Fourier space, using semi-implicit spectral method, it has been demonstrated that it allows faster computing than schemes based on finite difference method. First, spinodal decomposition of a given binary alloy under isothermal loading is implemented and three time stepping approaches are applied: constant time stepping, non- iterative and an iterative method. While the non-iterative method is faster than the constant time stepping scheme, the iterative one, although relatively more CPU consuming, can guarantee the convergence of the computing. These methods are combined in an innovative approach tested on 1D, 2D and 3D grids. The effectiveness of the adopted adaptive time-stepping algorithm allows resolving equations in reasonable CPU time. It predicts different physical phenomena, such as phase separation and growth and coarsening of precipitates induced by important interfacial energies.Métallurgie et Science des Matériaux (MMS

Adaptive time stepping approach for Phase-Field modeling of phase separation and precipitates coarsening in additive manufacturing alloys

peer reviewedDuring a Selective Laser Melting (SLM) process, applied thermal cycles and solidification velocities are considerablyincreasedwhen compared to ordinary directional solidification. That results in a very fine cellular-dendritic, out-of-equilibrium and inhomogeneous microstructure. Indeed, the SLM process can be described as a highly non-linear problem depending on various phenomena such as heat transfer, fluid flow, moving boundaries and crystalline anisotropy [1]. AlSi10Mg alloy additively manufactured is widely used in automotive and aerospace.The prediction of its long-term behavior is of interest and strongly linked to its original state after solidification and heat treatment.Standard analytical methods are not enough to assess the mechanical and thermophysical properties of the formed microstructure. The aim of this work is to apply a phase-field method combined with Calphad calculations to predict these properties after a heat treatment applied on the as-built sample as a stress release operation. The implemented phase-field model is based on Kim-Kim-Suzuki model [1] for the expression of the free energy as a function of the chemical composition. Moreover, the elastic energy in the system due to the volume misfit between the precipitates and the matrix is here considered. Its results are post-processed to provide thermo-physical properties and mechanical properties. These values based on predictions are compared to the ones deduced from experimental measurement in particular differential scanning calorimetry (DSC), dilatometry, laser flash diffusivimetry (LFA) and micro or nano-indentation. Once validated, the methodology will offer a quick and efficient prediction for different thermal post treatments

2D thermal finite element analysis of laser cladding of 316L+WC Composite coatings

peer reviewedIn this work, a 2D-thermal model of laser cladding (also called Directed Energy Deposition) of composite coating (316L stainless steel reinforced by hard WC carbide particles) was developed. The temperature field and its time evolution were computed by the Finite Element software Lagamine and then compared to experimental measurements. Indeed, in a related work, the effects of the high temperatures on the WC particles in contact with the molten metal and the resulting microstructure at the end of the fabrication were evaluated by means of different experimental techniques. Thus, correlations between simulated thermal histories and microscopic analysis as well as thermocouple records were established. The temperature distribution in the substrate allows the prediction of the depths of the melt pool as well as the thermal histories of the different parts of the clad. Such a model is of great interest and can be applied in situ-calculations to offer quick data about the influences of the process parameters on the properties of the built part.Metallic Materials Science (MMS

Data-driven Prediction of Temperature Evolution in Metallic Additive Manufacturing Process

peer reviewedIn this study, a data-driven deep learning model for fast and accurate prediction of temperature evolution and melting pool size of metallic additive manufacturing processes are developed. The study focuses on bulk experiments of the M4 high-speed steel material powder manufactured by Direct Energy Deposition. Under non-optimized process parameters, many deposited layers (above 30) generate large changes of microstructure through the sample depth caused by the high sensitivity of the cladding material on the thermal history. A 2D finite element analysis (FEA) of the bulk sample, validated in a previous study by experimental measurements, is able to achieve numerical data defining the temperature field evolution under different process settings. A Feed-forward neural networks (FFNN) approach is trained to reproduce the temperature fields generated from FEA. Hence, the trained FFNN is used to predict the history of the temperature fields for new process parameter sets not included in the initial dataset. Besides the input energy, nodal coordinates, and time, five additional features relating layer number, laser location, and distance from the laser to sampling point are considered to enhance prediction accuracy. The results indicate that the temperature evolution is predicted well by the FFNN with an accuracy of 99% within 12 seconds.EDPOM