Simulation Aided Process Development with Multi-Spot Strategies in Laser Powder-Bed Fusion

2021 ◽  
Vol 1161 ◽  
pp. 75-82
Author(s):  
Marcel Slodczyk ◽  
Alexander Ilin ◽  
Thomas Kiedrowski ◽  
Jens Schmiemann ◽  
Vasily Ploshikhin
Keyword(s):  
Laser Power ◽  
Process Development ◽  
Research Work ◽  
Spatial Arrangement ◽  
Quality Level ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Single Spot ◽  
Melt Pool

A challenge in laser powder-bed fusion is to achieve high process speed while maintaining quality level of the melting tracks. One approach to increase productivity is to distribute available laser power over several laser spots, resulting in higher melting rate. Using multiple laser spots opens up new parameter spaces in comparison to the conventional single-spot exposure. In addition to classical process parameters, e.g. total laser power and scanning speed, the distribution of power to the specific spots and the respective spatial arrangement have an impact on resulting process quality and speed. Within the scope of this research work, a physically based model is presented to define multi-spot process strategies for the generation of desired melt pool dimensions. Diffractive optical elements are used in order to adjust power or spatial arrangement of multiple laser spots. Resulting melt pool has more width and less depth compared to single-spot generated melt pools. Simulations and experiments show an optimum in applied spot distance between laser spots to obtain higher melting rates.

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.

Physics-Informed Gaussian Process Based Optimal Control of Laser Powder Bed Fusion

2020 ◽  
Author(s):  
Yong Ren ◽  
Qian Wang
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Gaussian Process ◽  
Laser Power ◽  
Reference Value ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

Abstract Regulating the melt-pool size to a constant reference value during the build process is a challenging task in Laser Powder Bed Fusion additive manufacturing (LPBF-AM). This paper considers adjusting laser power to achieve a constant melt-pool volume during laser processing of a multi-track build under LPBF-AM. First, a Gaussian Process Regression (GPR) is applied to model the variation of the melt-pool volume along the deposition distance, with physics-informed input features. Then a constrained finite-horizon optimal control problem is formulated, with a quadratic cost function defined to minimize the difference between the melt-pool volume and a reference value. A projected gradient descent algorithm is applied to compute the sequence of laser power in the proposed optimal control problem. The GPR modeling of melt-pool dynamics is trained and tested using simulated data sets generated from a commercial finite-element based AM software, and the same commercial AM software is used to evaluate the control performance. Simulation results demonstrate the effectiveness of the proposed GPR modeling and optimal control in regulating melt-pool volume for building multi-track parts with LPBF-AM.

scholarly journals A Convolutional Neural Network for Prediction of Laser Power Using Melt-Pool Images in Laser Powder Bed Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 23255-23263 ◽  
Author(s):  
Ohyung Kwon ◽  
Hyung Giun Kim ◽  
Wonrae Kim ◽  
Gun-Hee Kim ◽  
Kangil Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Power ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

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 Melt Pool Features Analysis Using a High Speed Coaxial Monitoring System for Laser Powder Bed Fusion of Ti-6Al-4V Grade 23

2021 ◽  
Author(s):  
Aditi Thanki ◽  
Louca Goossens ◽  
Agusmian Partogi Ompusunggu ◽  
Mohamad Bayat ◽  
Abdellatif Bey-Temsamani ◽  
...  
Keyword(s):  
Energy Density ◽  
Laser Power ◽  
High Speed ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Porosity Formation ◽  
Powder Bed ◽  
Binary Images ◽  
Melt Pool ◽  
Scan Speed

Abstract In laser powder bed fusion (LPBF), defects such as pores or cracks can seriously affect the final part quality and lifetime. Keyhole porosity, being one type of porosity defects in LPBF, results from excessive energy density which may be due to changes in process parameters (laser power and scan speed) and/or result from the part’s geometry and/or hatching strategies. To study the possible occurrence of keyhole pores, experimental work as well as simulations were carried out for optimum and high volumetric energy density conditions in Ti-6Al-4V grade 23. By decreasing the scanning speed from 1000 mm/s to 500 mm/s for a fixed laser power of 170 W, keyhole porosities are formed and later observed by X-ray computed tomography. Melt pool images are recorded in real-time during the LPBF process by using a high speed coaxial Near-Infrared (NIR) camera monitoring system. The recorded images are then pre-processed using a set of image processing steps to generate binary images. From the binary images, geometrical features of the melt pool and features that characterize the spatter particles formation and ejection from the melt pool are calculated. The experimental data clearly show spatter patterns in case of keyhole porosity formation at low scan speed. A correlation between the number of pores and the amount of spatter is observed. Besides the experimental work, a previously developed, high fidelity finite volume numerical model was used to simulate the melt pool dynamics with similar process parameters as in the experiment. Simulation results illustrate and confirm the keyhole porosity formation by decreasing laser scan speed.

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 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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