America Makes: National Additive Manufacturing Innovation Institute (NAMII) Project 1: Nondestructive Evaluation (NDE) of Complex Metallic Additive Manufactured (AM) Structures

2014 ◽  
Author(s):  
Evgueni Todorov ◽  
Roger Spencer ◽  
Sean Gleeson ◽  
Madhi Jamshidinia ◽  
Shawn M. Kelly
Keyword(s):  
Additive Manufacturing ◽  
Nondestructive Evaluation ◽  
Manufacturing Innovation ◽  
Metallic Additive

scholarly journals Numerical simulation on metallic additive manufacturing

2020 ◽  
Vol 50 (9) ◽  
pp. 090007
Author(s):  
FeiYu XIONG ◽  
JiaWei CHEN ◽  
ChenYang HUANG ◽  
YanPing LIAN
Keyword(s):  
Numerical Simulation ◽  
Additive Manufacturing ◽  
Metallic Additive

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.

Investigations into effect of weld-deposition pattern on residual stress evolution for metallic additive manufacturing

2016 ◽  
Vol 90 (5-8) ◽  
pp. 2009-2025 ◽  
Author(s):  
M. A. Somashekara ◽  
M. Naveenkumar ◽  
Avinash Kumar ◽  
C. Viswanath ◽  
S. Simhambhatla
Keyword(s):  
Residual Stress ◽  
Additive Manufacturing ◽  
Stress Evolution ◽  
Deposition Pattern ◽  
Metallic Additive

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 Thermal Monitoring for Metallic Additive Manufacturing Multi-Beads Multi-Layers Parts

2021 ◽  
Author(s):  
Matthieu Rauch ◽  
Jean-Yves Hascoet ◽  
Clement Rousseau ◽  
Guillaume Ruckert
Keyword(s):  
Additive Manufacturing ◽  
Thermal Monitoring ◽  
Metallic Additive

scholarly journals Metallic Additive Manufacturing: Design, Process, and Post-Processing

Metals ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 137 ◽  
Author(s):  
Ian Gibson ◽  
Amir Khorasani
Keyword(s):  
Additive Manufacturing ◽  
Design Process ◽  
Post Processing ◽  
Manufacturing Design ◽  
Metallic Additive

The first modern additive manufacturing machines, developed in the early 1990s, primarily made parts using polymers [...]

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