A Reduced Gaussian Process Heat Emulator for Laser Powder Bed Fusion

2021 ◽  
pp. 285-293
Author(s):  
Xiaohan Li ◽  
Nick Polydorides
Keyword(s):  
Gaussian Process ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Process Heat

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 Automatic Quality Assessments of Laser Powder Bed Fusion Builds from Photodiode Sensor Measurements

2020 ◽  
Author(s):  
Sarini Jayasinghe ◽  
Paolo Paoletti ◽  
Chris Sutcliffe ◽  
John Dardis ◽  
Nick Jones ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Learning Algorithms ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Part Density

This study evaluates whether a combination of photodiode sensor measurements, taken during laser powder bed fusion (L-PBF) builds, can be used to predict the resulting build quality via a purely data-based approach. We analyse the relationship between build density and features that are extracted from sensor data collected from three different photodiodes. The study uses a Singular Value Decomposition to extract lower-dimensional features from photodiode measurements, which are then fed into machine learning algorithms. Several unsupervised learning methods are then employed to classify low density (< 99% part density) and high density (≥ 99% part density) specimens. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density. Using the unsupervised clustering approaches, applied to features extracted from both photodiode sensor data as well as observations relating to the energy transferred to the material, build density was predicted with up to 93.54% accuracy. With regard to the supervised regression approach, a Gaussian Process algorithm was capable of predicting the build density with a RMS error of 3.65%. The study shows, therefore, that there is potential for machine learning algorithms to predict indicators of L-PBF build quality from photodiode build-measurements. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantification in machine-learnt predictions of L-PBF build quality.

scholarly journals Performance Characterization of Laser Powder Bed Fusion Fabricated Inconel 718 Treated with Experimental Hot Isostatic Processing Cycles

2020 ◽  
Vol 4 (3) ◽  
pp. 73 ◽  
Author(s):  
Jaime Varela ◽  
Jorge Merino ◽  
Christina Pickett ◽  
Ahmad Abu-Issa ◽  
Edel Arrieta ◽  
...  
Keyword(s):  
Grain Boundaries ◽  
Yield Stress ◽  
Inconel 718 ◽  
Solution Treatment ◽  
Stress Relief ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Post Process ◽  
Process Heat

Inconel 718 alloy fabricated by selective laser melting (SLM) (or laser powder-bed fusion (LPBF)) has been post-process heat-treated by stress-relief anneal at 1065 °C; stress-relief anneal (1065 °C) + solution treatment (at 720 °C) + aging (at 620 °C); hot isostatic pressing (HIP) (at 1120–1200 °C); stress-relief anneal + HIP; and stress-relief anneal + HIP + solution treatment + aging. Microstructure analysis utilizing optical metallography revealed primarily equiaxed grain structures (having average diameters ranging from ~30 to 49 microns) containing annealing twins, and a high concentration of carbide precipitates in all HIP-related treatments in the grain boundaries and intragrain regions. However, no precipitates nucleated on the {111} coherent annealing twin boundaries because of their very low interfacial free energy in contrast to regular grain boundaries. The mechanical properties for the as-fabricated Inconel 718 exhibited a yield stress of 0.64 GPa, UTS of 0.98 GPa, and elongation of 26%. Following stress-relief anneal at 1065 °C, the yield stress dropped to 0.60 GPa, while the elongation increased to 43%. The associated grain structure was an irregular, somewhat elongated, recrystallized structure. This structure was preserved at a stress anneal at 1065 °C + solution treatment + aging, but grain boundary and intragrain precipitation resulted in a doubling of the yield stress to 1.3 GPa and a reduced elongation of 12.6%. The results of HIP-related post-process heat treatments involving temperatures above 1060 °C demonstrated that the yield stress and elongations could be varied from 1.07 to 1.17 GPa and 11.4% to 19%, respectively. Corresponding Rockwell C-scale hardness values also varied from 33 for the as-fabricated Inconel 718 to 53 for simple post-process HIP treatment at 1163 °C.

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.

Heat source model calibration for thermal analysis of laser powder-bed fusion

2020 ◽  
Vol 106 (7-8) ◽  
pp. 3367-3379 ◽  
Author(s):  
Shahriar Imani Shahabad ◽  
Zhidong Zhang ◽  
Ali Keshavarzkermani ◽  
Usman Ali ◽  
Yahya Mahmoodkhani ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Heat Source ◽  
Model Calibration ◽  
Source Model ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Heat Source Model ◽  
Powder Bed
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