scholarly journals Laser Powder Bed Fusion (LPBF) of In718 and the Impact of Pre-Heating at 500 and 1000 °C: Operando Study

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6683
Author(s):  
Asif Ur Rehman ◽  
Fatih Pitir ◽  
Metin Uymaz Salamci
Keyword(s):  
Critical Role ◽  
Simulation Models ◽  
Parameters Optimization ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Physical Processes ◽  
Mesoscopic Simulation ◽  
Powder Bed ◽  
Melt Pool ◽  
The Impact

The morphology of a melt pool has a critical role in laser powder bed fusion (LPBF). Nevertheless, directly characterizing the melt pool during LPBF is incredibly hard. Here, we present the melt pool flow of the entire melt pool in 3D using mesoscopic simulation models. The physical processes occurring within the melt pool are pinpointed. The flow patterns throughout the same are exposed and measured. Moreover, the impact of pre-heating at 500 and 1000 °C has been described. The study findings offer insights into LPBF. The findings presented here are critical for comprehending the LPBF and directing the establishment of improved metrics for process parameters optimization.

scholarly journals Numerical study of the impact of vaporisation on melt pool dynamics in Laser Powder Bed Fusion - Application to IN718 and Ti–6Al–4V

2020 ◽  
Vol 35 ◽  
pp. 101249
Author(s):  
Alexis Queva ◽  
Gildas Guillemot ◽  
Clara Moriconi ◽  
Charlotte Metton ◽  
Michel Bellet
Keyword(s):  
Numerical Study ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
The Impact

scholarly journals Optimization of Laser Powder Bed Fusion Processing Using a Combination of Melt Pool Modeling and Design of Experiment Approaches: Density Control

2019 ◽  
Vol 3 (1) ◽  
pp. 21 ◽  
Author(s):  
Morgan Letenneur ◽  
Alena Kreitcberg ◽  
Vladimir Brailovski
Keyword(s):  
Scanning Speed ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Density Prediction ◽  
Melt Pool ◽  
Density Control ◽  
Pool Model ◽  
Conventional Material ◽  
Prediction Approach

A simplified analytical model of the laser powder bed fusion (LPBF) process was used to develop a novel density prediction approach that can be adapted for any given powder feedstock and LPBF system. First, calibration coupons were built using IN625, Ti64 and Fe powders and a specific LPBF system. These coupons were manufactured using the predetermined ranges of laser power, scanning speed, hatching space, and layer thickness, and their densities were measured using conventional material characterization techniques. Next, a simplified melt pool model was used to calculate the melt pool dimensions for the selected sets of printing parameters. Both sets of data were then combined to predict the density of printed parts. This approach was additionally validated using the literature data on AlSi10Mg and 316L alloys, thus demonstrating that it can reliably be used to optimize the laser powder bed metal fusion process.

A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion

2021 ◽  
Author(s):  
Yong Ren ◽  
Qian Wang ◽  
Panagiotis (Pan) Michaleris
Keyword(s):  
Machine Learning ◽  
Initial Temperature ◽  
Pool Size ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Input Feature ◽  
Melt Pool Size ◽  
Melt Pool Characteristics

Abstract Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.

Measurement of the Melt Pool Length During Single Scan Tracks in a Commercial Laser Powder Bed Fusion Process

2017 ◽  
Author(s):  
J. C. Heigel ◽  
B. M. Lane
Keyword(s):  
Laser Power ◽  
High Speed ◽  
Infrared Camera ◽  
Powder Bed Fusion ◽  
Single Track ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Significant Difference ◽  
Scan Speed

This work presents high speed thermographic measurements of the melt pool length during single track laser scans on nickel alloy 625 substrates. Scans are made using a commercial laser powder bed fusion machine while measurements of the radiation from the surface are made using a high speed (1800 frames per second) infrared camera. The melt pool length measurement is based on the detection of the liquidus-solidus transition that is evident in the temperature profile. Seven different combinations of programmed laser power (49 W to 195 W) and scan speed (200 mm/s to 800 mm/s) are investigated and numerous replications using a variety of scan lengths (4 mm to 12 mm) are performed. Results show that the melt pool length reaches steady state within 2 mm of the start of each scan. Melt pool length increases with laser power, but its relationship with scan speed is less obvious because there is no significant difference between cases performed at the highest laser power of 195 W. Although keyholing appears to affect the anticipated trends in melt pool length, further research is required.

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