Predicting and Controlling the Thermal Part History in Powder Bed Fusion Using Neural Networks

2021 ◽  
Author(s):  
Holger Merschroth ◽  
Matthias Weigold ◽  
Michael Kniepkamp
Keyword(s):  
Neural Networks ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Thermal Part

Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks

2020 ◽  
Vol 16 (9) ◽  
pp. 5769-5779
Author(s):  
Yingjie Zhang ◽  
Hong Geok Soon ◽  
Dongsen Ye ◽  
Jerry Ying Hsi Fuh ◽  
Kunpeng Zhu
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Convolutional Neural Networks ◽  
Process Monitoring ◽  
Fusion Process ◽  
Powder Bed Fusion ◽  
Powder Bed

scholarly journals Automated porosity assessment of parts produced by Laser Powder Bed Fusion using Convolutional Neural Networks

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 1434-1439
Author(s):  
Jan Klein ◽  
Martin Jaretzki ◽  
Michael Schwarzenberger ◽  
Steffen Ihlenfeldt ◽  
Welf-Guntram Drossel
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed

Stochastically-Trained Physics-Informed Neural Networks: Application to Thermal Analysis in Metal Laser Powder Bed Fusion

2021 ◽  
Author(s):  
Justin Pierce ◽  
Glen Williams ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel ◽  
Christopher McComb
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Thermal Analysis ◽  
Surrogate Models ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Limiting Factor ◽  
Powder Bed Fusion ◽  
Powder Bed

Abstract Modern digital manufacturing processes, such as additive manufacturing, are cyber-physical in nature and utilize complex, process-specific simulations for both design and manufacturing. Although computational simulations can be used to optimize these complex processes, they can take hours or days — an unreasonable cost for engineering teams leveraging iterative design processes. Hence, more rapid computational methods are necessary in areas where computation time presents a limiting factor. When existing data from historical examples is plentiful and reliable, supervised machine learning can be used to create surrogate models that can be evaluated orders of magnitude more rapidly than comparable finite element approaches. However, for applications that necessitate computationally-intensive simulations, even generating the training data necessary to train a supervised machine learning model can pose a significant barrier. Unsupervised methods, such as physics-informed neural networks, offer a shortcut in cases where training data is scarce or prohibitive. These novel neural networks are trained without the use of potentially expensive labels. Instead, physical principles are encoded directly into the loss function. This method substantially reduces the time required to develop a training dataset, while still achieving the evaluation speed that is typical of supervised machine learning surrogate models. We propose a new method for stochastically training and testing a convolutional physics-informed neural network using the transient 3D heat equation- to model temperature throughout a solid object over time. We demonstrate this approach by applying it to a transient thermal analysis model of the powder bed fusion manufacturing process.

Grain Morphology and Texture Development in a Co-Cr-Mo Alloy Fabricated by Powder Bed Fusion with an Electron Beam

2019 ◽  
Author(s):  
Yufan Zhao ◽  
Yuichiro Koizumi ◽  
Kenta Aoyagi ◽  
Daixiu Wei ◽  
Kenta Yamanaka ◽  
...  
Keyword(s):  
Electron Beam ◽  
Grain Morphology ◽  
Texture Development ◽  
Powder Bed Fusion ◽  
Powder Bed

scholarly journals On the Relevance of Volumetric Energy Density in the Investigation of Inconel 718 Laser Powder Bed Fusion

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 538 ◽  
Author(s):  
Fabrizia Caiazzo ◽  
Vittorio Alfieri ◽  
Giuseppe Casalino
Keyword(s):  
Surface Roughness ◽  
Energy Density ◽  
Process Parameters ◽  
Vickers Hardness ◽  
Powder Bed Fusion ◽  
Processing Conditions ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Fractional Density ◽  
Experimental Variation

Laser powder bed fusion (LPBF) can fabricate products with tailored mechanical and surface properties. In fact, surface texture, roughness, pore size, the resulting fractional density, and microhardness highly depend on the processing conditions, which are very difficult to deal with. Therefore, this paper aims at investigating the relevance of the volumetric energy density (VED) that is a concise index of some governing factors with a potential operational use. This paper proves the fact that the observed experimental variation in the surface roughness, number and size of pores, the fractional density, and Vickers hardness can be explained in terms of VED that can help the investigator in dealing with several process parameters at once.

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