Microstructural evolution of 9Cr-1Mo steel during long term exposure at 550°C

The 9%Cr high resistant steels, especially the modified 9Cr-1Mo steel (T91), have proved good performances in thermal power plants, petrochemical industries and nuclear reactors since the eighties. These performances can be traduced by the high creep strength, the resistance to oxidation cracking and the adequate cost. Thermal exposure changes of the microstructure of the modified 9Cr-1Mo steel have been investigated, through long term experience at 550°C in furnace, up to 7000 hours. Detailed analysis of the microstructural evolution and changes in secondary carbides (M23C6) were carried out using SEM, TEM, XRD and EDX analysis. Electrochemical extractions were done because of the small volume of carbides. A progressive restoration of the tempered martensite matrix was observed. Moreover, a continuous increase of M23C6 size is revealed until stabilization after about 5000 hours of exposure. The nucleation of Laves phases is here found; two inverse contributions may be concluded. When nucleating far from secondary precipitates, these phases grow by consuming matrix elements, which can trigger creep damages. Nevertheless, by surrounding the M23C6 carbides like a shell, Laves phases can slow down their growth and so contribute in solid solution hardening.  X-ray diffraction analysis lead to determine the temperature-time dependence of the matrix and M23C6 lattice parameter

Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]

peer reviewedThe passage describes the development of a numerical framework in Python to create and process a large dataset for time-series prediction using Deep Learning algorithms. The dataset is generated by solving the Cahn–Hilliard equation for spinodal decomposition of a binary alloy and is labeled to train the algorithms. Prior to training, dimensionality reduction is performed using Auto-encoders and Principal Component Analysis. The framework identifies three distinct latent dimensions/spaces for the datasets. The primary dataset was generated by running up to 10,000 High-Fidelity Phase-Field simulations in parallel using High-Performance Computing (HPC). The framework is compatible with all major operating systems and has been thoroughly tested on Python 3.7 and later versions

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

In 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

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

peer reviewedLongLifeA

Uncertainty Quantification in the Directed Energy Deposition Process Using Deep Learning-Based Probabilistic Approach

peer reviewedThis study quantifies the effects of uncertainty raised from process parameters, material properties, and boundary conditions in the directed energy deposition (DED) process of M4 High-Speed Steel using deep learning (DL)-based probabilistic approach. A DL-based surrogate model is first constructed using the data obtained from a finite element (FE) model, which was validated against experiment. Then, sources of uncertainty are characterized by the probabilistic method and are propagated by the Monte-Carlo (MC) method. Lastly, the sensitivity analysis (SA) using the variance-based method is performed to identify the parameters inducing the most uncertainty to the melting pool depth. Using the DL-based surrogate model instead of solely FE model significantly reduces the computational time in the MC simulation. The results indicate that all sources of uncertainty contribute to a substantial variation on the final printed product quality. Moreover, we find that the laser power, the convection, the scanning speed, and the thermal conductivity contribute the most uncertainties on the melting pool depth based on the SA results. These findings can be used as insights for the process parameter optimization of the DED process.EDPOM

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

Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning

Typical computer-based parameter optimization and uncertainty quantification of the additive manufacturing process usually requires significant computational cost for performing high-fidelity heat transfer finite element (FE) models with different process settings. This work develops a simple surrogate model using a feedforward neural network (FFNN) for a fast and accurate prediction of the temperature evolutions and the melting pool sizes in a metal bulk sample (3D horizontal layers) manufactured by the DED process. Our surrogate model is trained using high-fidelity data obtained from the FE model, which was validated by experiments. The temperature evolutions and the melting pool sizes predicted by the FFNN model exhibit accuracy of 99% and 98%, respectively, compared with the FE model for unseen process settings in the studied range. Moreover, to evaluate the importance of the input features and explain the achieved accuracy of the FFNN model, a sensitivity analysis (SA) is carried out using the SHapley Additive exPlanation (SHAP) method. The SA shows that the most critical enriched features impacting the predictive capability of the FFNN model are the vertical distance from the laser head position to the material point and the laser head position.VINIF.2020.DA15 EDPOMP projec

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