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

2021 ◽  
Author(s):  
Quy Duc Thinh Pham ◽  
Truong Vinh Hoang ◽  
Quoc Tuan Pham ◽  
Than Phuc Huynh ◽  
Van Xuan Tran ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
High Speed ◽  
High Speed Steel ◽  
Numerical Data ◽  
Temperature Evolution ◽  
Data Driven ◽  
Temperature Fields ◽  
Sampling Point ◽  
Element Analysis ◽  
Metallic Additive

In 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.

scholarly journals Selective Laser Melting of Crack-Free High Density M2 High Speed Steel Parts by Baseplate Preheating

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Karolien Kempen ◽  
Bey Vrancken ◽  
Sam Buls ◽  
Lore Thijs ◽  
Jan Van Humbeeck ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
High Speed ◽  
Crack Formation ◽  
High Speed Steel ◽  
Thermal Gradients ◽  
Laser Melting ◽  
Mechanical And Physical Properties ◽  
The World ◽  
M2 High Speed Steel

Cracks and delamination, resulting from residual stresses, are a barrier in the world of additive manufacturing and selective laser melting (SLM) that prohibits the use of many metals in this field. By preheating the baseplate, thermal gradients are lowered and stresses can be reduced. In this work, some initial tests were performed with M2 high speed steel (HSS). The influence of preheating on density and mechanical and physical properties is investigated. The paper shows many promising results for the production of SLM parts in materials that are very sensitive to crack formation and delamination. When using a preheating of 200 °C, crack-free M2 HSS parts were produced with a relative density of 99.8%.

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

2020 ◽  
Author(s):  
Zhuo Wang ◽  
Chen Jiang ◽  
Mark F. Horstemeyer ◽  
Zhen Hu ◽  
Lei Chen
Keyword(s):  
Temperature Field ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Surrogate Modeling ◽  
Data Driven ◽  
Modeling Framework ◽  
High Quality ◽  
Experimental Calibration ◽  
Multi Scale ◽  
Metallic Additive

Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.

Analysis of the vertical moving table type broaching machine

2020 ◽  
Vol 14 (3) ◽  
pp. 7152-7169
Author(s):  
Okka Adiyanto ◽  
Park In Soo ◽  
Angga Senoaji Hermanto ◽  
Choi Won Sik
Keyword(s):  
High Speed ◽  
Structural Characteristics ◽  
High Speed Steel ◽  
Frictional Stress ◽  
Element Analysis ◽  
Friction Characteristic ◽  
Maximum Deformation ◽  
Hydraulic Machines ◽  
Speed Range ◽  
Physical Prototype

Broaching is a type of machining that uses a toothed tool similar to a saw. There are several types of broaching machines includes linear broaching machines and hydraulic machines.  Early linear broaching machines were driven mechanically by screws. However, hydraulic machines are faster, smoother in operation, and allow for high-speed steel broaches to be used. The purpose of this study is to an analysis of the vertical moving table type in the broaching machine. In this study, finite element analysis was carried out to examine the structural characteristics of broaching machine design. A model was created in CATIA software and analyzed with ANSYS to find the structural characteristics. The friction characteristic of PBT-40 material was also investigated. This material is recommended for guide rail surface lamination to reduce the friction coefficient and ram body wear. The simulation results provide information for the next step of development before physical prototype will be made. The maximum deformation of the workpiece table was 0.0517 mm on the positive Z-axis, and the maximum deformation on the pulling head device was 0.0598 mm on the negative Z-axis. The friction coefficients were between 0.013 and 0.047 in the sliding speed range of 0.06 to 0.34 m/s. The PBT-40 material has a wear coefficient of 1.604x10-13 m3/Nm according to the test. From the ANSYS friction simulation, it can be concluded that the PBT-40 material would not easily wear out during operation of the machine. It can be seen that small frictional stress occurred on the surface ranging from 8.273x10-5 to 8.381x10-5 MPa.

Laser additive manufacturing of M2 high-speed steel

2017 ◽  
Vol 34 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Min Zhang ◽  
Changjun Chen ◽  
Lanlan Qin ◽  
Kai Yan ◽  
Guangping Cheng ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
High Speed ◽  
High Speed Steel ◽  
Laser Additive Manufacturing ◽  
M2 High Speed Steel

Temperature effect on service performance of high-speed railway concrete bridges

2016 ◽  
Vol 20 (6) ◽  
pp. 865-883 ◽  
Author(s):  
Y Tian ◽  
N Zhang ◽  
H Xia
Keyword(s):  
Temperature Effect ◽  
High Speed ◽  
Low Frequency ◽  
Numerical Approach ◽  
Service Performance ◽  
Dynamic Responses ◽  
Temperature Fields ◽  
Coupled Analysis ◽  
Element Analysis ◽  
High Speed Railway

Non-uniform temperature fields induced by time-varying solar position and heat exchange are of great significance for the bridge safety. The accurate identifications of these changes are necessary to avoid unexpected deformations and the loss of service performance. This article presents a numerical approach to determine temperature effects on train–bridge-coupled dynamics. Heat flux density of different components of a 32-m simply supported concrete bridge on high-speed railway is calculated, in which a section segmentation method is adopted for simplifications of boundary conditions. Based on heat–stress-coupled technology, temperature fields and deformation fields of the bridge are then computed via finite element analysis. Combining track irregularities and its thermal deformation as external excitations, the train–bridge-coupled analysis is solved by an inter-system iteration method. Dynamic responses of bridge and train are compared to those obtained neglecting the temperature effect. Comparative studies illustrate that temperature effect has major impacts on the bridge displacement due to the increase in low-frequency components of excitations. For the train, lateral responses are more obvious. Maximum derail factor and lateral wheel–rail force occur when the train leaves from the bridge.

scholarly journals Element activation method and non-conformal dynamic remeshing strategy to model additive manufacturing

2021 ◽  
Author(s):  
Cédric Laruelle ◽  
Romain Boman ◽  
Luc Papeleux ◽  
Jean-Philippe Ponthot
Keyword(s):  
Additive Manufacturing ◽  
High Speed ◽  
Data Transfer ◽  
Computational Cost ◽  
High Speed Steel ◽  
Projection Methods ◽  
Fully Implicit ◽  
Direct Energy ◽  
Deletion Algorithm ◽  
The University

Modeling of Additive Manufacturing (AM) at the part scale involves non-linear thermo-mechanical simulations. Such a process also imposes a very fine discretization and requires altering the geometry of the models during the simulations to model the addition of matter, which is a computational challenge by itself. The first focus of this work is the addition of an additive manufacturing module in the fully implicit in-house Finite Element code Metafor [1] which is developed at the University of Liège. The implemented method to activate elements and to activate and deactivate boundary conditions during a simulation is adapted from the element deletion algorithm implemented in Metafor in the scope of crack propagation [2]. This algorithm is modified to allow the activation of elements based on a user-specified criterion (e.g. geometrical criterion, thermal criterion, etc.). The second objective of this work is to improve the efficiency of the AM simulations, in particular by using a dynamic remeshing strategy to reduce the computational cost of the simulations. This remeshing is done using non-conformal meshes, where hanging nodes are handled via the use of Lagrange multiplier constraints. The mesh data transfer used after remeshing is based on projection methods involving finite volumes [3]. The presented model is then compared against a 2D numerical simulation of Direct Energy Deposition of a High-Speed Steel thick deposit from the literature [4].

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