Camera-Based Coaxial Melt Pool Monitoring Data Registration for Laser Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Yan Lu ◽  
Zhuo Yang ◽  
Jaehyuk Kim ◽  
Hyunbo Cho ◽  
Ho Yeung
Keyword(s):  
Coordinate System ◽  
Open Architecture ◽  
Powder Bed Fusion ◽  
Real Time Control ◽  
Powder Bed ◽  
Data Registration ◽  
Time Control ◽  
Melt Pool ◽  
Highly Correlated ◽  
Melt Pool Characteristics

Abstract The quality of powder bed fusion (PBF) built parts is highly correlated to the melt pool characteristics. Camera-based coaxial melt pool monitoring (MPM) is widely applied today because it provides high-resolution monitoring on the time and length scales necessary for deep PBF process understanding, in-process defect detection, and real-time control. For such functions, MPM data has to be registered correctly to a well-defined coordinate system. This paper presents methods for camera-based coaxial melt pool monitoring (MPM) data registration using the build volume coordinate system defined in ISO/ASTM52921, for both open architecture AM systems and 3rd party MPM augmented closed commercial systems. Uncertainties are evaluated for the proposed methods and case studies provided to demonstrate the effectiveness of the methods.

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 science needs for real-time control of additive manufacturing powder-bed fusion processes

2017 ◽  
pp. 629-652 ◽  
Author(s):  
Mahesh Mani ◽  
Shaw Feng ◽  
Lane Brandon ◽  
Alkan Donmez ◽  
Shawn Moylan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Powder Bed Fusion ◽  
Real Time Control ◽  
Powder Bed ◽  
Time Control ◽  
Measurement Science

scholarly journals In situ alloying: investigation of the melt pool stability during powder bed fusion of metals using a laser beam in a novel experimental set-up

2021 ◽  
Author(s):  
Andreas Wimmer ◽  
Fabian Hofstaetter ◽  
Constantin Jugert ◽  
Katrin Wudy ◽  
Michael F. Zaeh
Keyword(s):  
Laser Beam ◽  
High Speed ◽  
Process Zone ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Limited Access ◽  
Pool Depth ◽  
Melt Pool ◽  
Melt Pool Characteristics

AbstractThe limited access to materials for the Powder Bed Fusion of Metals using a Laser Beam (PBF-LB/M) is compensated by in situ alloying. Individual melt pool characteristics can be specifically influenced to improve the mechanical properties of the final part. However, conventional PBF-LB/M machines allow only limited access for detailed observation of the process zone and, in particular, the melt pool. This paper presents a methodology for systematically analyzing the melt pool in the cross section to determine the in situ variation of the melt pool depth. A custom PBF-LB/M test bench was devised to enable investigation of the process zone using high-speed infrared cameras. The image data were processed automatically using a dedicated algorithm. The methodology was applied to analyze the effect of additives on the melt pool stability. Stainless steel 316L powder was blended with the aluminum alloy AlSi10Mg by up to 20 wt.%. It was found that the blended powder significantly reduced the variation of the melt pool depth.

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.

Sign in / Sign up

Export Citation Format

Share Document