scholarly journals Full-Field Mapping and Flow Quantification of Melt Pool Dynamics in Laser Powder Bed Fusion of SS316L

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6264
Author(s):  
Asif Ur Rehman ◽  
Fatih Pitir ◽  
Metin Uymaz Salamci
Keyword(s):  
Flow Quantification ◽  
High Tech ◽  
Powder Bed Fusion ◽  
Single Track ◽  
Laser Powder Bed Fusion ◽  
Full Field ◽  
Powder Bed ◽  
Field Mapping ◽  
Melt Pool ◽  
Wide Range

Laser powder bed fusion (LPBF) has a wide range of uses in high-tech industries, including the aerospace and biomedical fields. For LPBF, the flow of molten metal is crucial; until now, however, the flow in the melt pool has not been described thoroughly in 3D. Here, we provide full-field mapping and flow measurement of melt pool dynamics in laser powder bed fusion, through a high-fidelity numerical model using the finite volume method. The influence of Marangoni flow, evaporation, as well as recoil pressure have been included in the model. Single-track experiments were conducted for validation. The temperature profiles at different power and speed parameters were simulated, and results were compared with experimental temperature recordings. The flow dynamics in a single track were exposed. The numerical and experimental findings revealed that even in the same melting track, the melt pool’s height and width can vary due to the strong Marangoni force. The model showed that the variation in density and volume for the same melting track was one of the critical reasons for defects. The acquired findings shed important light on laser additive manufacturing processes and pave the way for the development of robust, computational models with a high degree of reliability.

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.

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

2018 ◽  
Vol 140 (5) ◽  
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 (PBF) 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 (49–195 W) and scan speed (200–800 mm/s) are investigated, and numerous replications using a variety of scan lengths (4–12 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.

scholarly journals An Efficient Track-Scale Model for Laser Powder Bed Fusion Additive Manufacturing: Part 1- Thermal Model

2021 ◽  
Vol 8 ◽  
Author(s):  
Reza Tangestani ◽  
Trevor Sabiston ◽  
Apratim Chakraborty ◽  
Waqas Muhammad ◽  
Lang Yuan ◽  
...  
Keyword(s):  
Heat Input ◽  
Deterministic Model ◽  
Scale Model ◽  
Computational Time ◽  
Transfer Model ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Full Field ◽  
Powder Bed ◽  
Melt Pool

This is the first of two manuscripts that presents a computationally efficient full field deterministic model for laser powder bed fusion (LPBF). A new Hybrid Line (HL) heat input model integrates an exponentially decaying (ED) heat input over a portion of a laser path to significantly reduce the computational time. Experimentally measured properties of the high gamma prime nickel-based superalloy RENÉ 65 are implemented in the model to predict the in-process temperature distribution, stresses, and distortions. The model accounts for specific properties of the material as different phases. The first manuscript presents the HL heat transfer model, which is compared with the beam-scale exponentially decaying model, along with the melt pool geometry obtained experimentally by varying the laser parameters. The predicted melt pool geometry of the beam-scale ED model is shown to have good agreement with experimental measurements. While the proposed HL model exhibits lesser accuracy in predicting the melt pool geometries, it can predict the cooling rates and nodal temperatures as accurately as to the ED model. Moreover, under large time integration steps, the HL model becomes more than 1,500 times faster than the ED model.

Real-time Image-based Feedback Control of Laser Powder Bed Fusion

2021 ◽  
pp. 1-12
Author(s):  
Aleksandr Shkoruta ◽  
Sandipan Mishra ◽  
Stephen Rock
Keyword(s):  
Real Time ◽  
Laser Power ◽  
Powder Bed Fusion ◽  
Single Track ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
Real Time Image ◽  
Time Image ◽  
Pool Image

Abstract This letter presents the design and experimental validation of a real-time image-based feedback control system for metal laser powder bed fusion (LPBF). A coaxial melt pool video stream is used to control laser power in real-time at 2 kHz. Modeling of the melt pool image response to changes in the input laser power is presented. Based on this identified model, a real-time feedback controller is implemented experimentally, on a single track and part scales. On a single-track scale, the controller successfully tracks a time-varying melt pool reference. On a part-level scale, the controller successfully regulates the melt pool image signature to the desired reference value, reducing layer-to-layer signal variation, and eliminating within-layer signal drift.

scholarly journals Process characterization and analysis of ceramic powder bed fusion

2021 ◽  
Author(s):  
Kevin Florio ◽  
Dario Puccio ◽  
Giorgio Viganò ◽  
Stefan Pfeiffer ◽  
Fabrizio Verga ◽  
...  
Keyword(s):  
High Speed ◽  
Ceramic Powder ◽  
Integrating Sphere ◽  
Alumina Powder ◽  
Powder Bed Fusion ◽  
Single Track ◽  
Ceramic Powders ◽  
Pure Alumina ◽  
Powder Bed ◽  
Melt Pool

AbstractPowder bed fusion (PBF) of ceramics is often limited because of the low absorptance of ceramic powders and lack of process understanding. These challenges have been addressed through a co-development of customized ceramic powders and laser process capabilities. The starting powder is made of a mix of pure alumina powder and alumina granules, to which a metal oxide dopant is added to increase absorptance. The performance of different granules and process parameters depends on a large number of influencing factors. In this study, two methods for characterizing and analyzing the PBF process are presented and used to assess which dopant is the most suitable for the process. The first method allows one to analyze the absorptance of the laser during the melting of a single track using an integrating sphere. The second one relies on in-situ video imaging using a high-speed camera and an external laser illumination. The absorption behavior of the laser power during the melting of both single tracks and full layers is proven to be a non-linear and extremely dynamic process. While for a single track, the manganese oxide doped powder delivers higher and more stable absorptance. When a full layer is analyzed, iron oxide-doped powder is leading to higher absorptance and a larger melt pool. Both dopants allow the generation of a stable melt-pool, which would be impossible with granules made of pure alumina. In addition, the present study sheds light on several phenomena related to powder and melt-pool dynamics, such as the change of melt-pool shape and dimension over time and powder denudation effects.

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.

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