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1. Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing

   https://doi.org/10.1115/1.4048957  

   Tian, Qi ; Guo, Shenghan ; Melder, Erika ; Bian, Linkan ; Guo, Weihong “Grace” ( April 2021 , Journal of Manufacturing Science and Engineering)

   null (Ed.)

   Abstract Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti–6Al–4V thin-wall structure. This is the first work that manages to fuse pyrometer data and infrared camera data for metal additive manufacturing (AM). The case study results based on benchmark datasets show that our method can achieve high accuracy with relatively high efficiency, demonstrating the applicability of the method for in situ porosity detection in LBAM. 

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2. Physics guided heat source for quantitative prediction of IN718 laser additive manufacturing processes

   https://doi.org/10.1038/s41524-024-01198-6  

   Amin, Abdullah Al ; Li, Yangfan ; Lu, Ye ; Xie, Xiaoyu ; Gan, Zhengtao ; Mojumder, Satyajit ; Wagner, Gregory J ; Liu, Wing Kam ( December 2024 , npj Computational Materials)

   Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developedAdditiveManufacturingComputationalFluidDynamics code (AM-CFD) combined with a cylindrical heat source is implemented to accurately predict these experiments. Heuristic heat source calibration is proposed relating volumetric energy density (ψ) based on experiments available in the literature. The parameters of the heat source of the computational model are initially calibrated based on a Higher Order Proper Generalized Decomposition- (HOPGD) based surrogate model. The prediction using the calibrated heat source agrees quantitatively with NIST measurements for different process conditions (laser spot diameter, laser power, and scan speed). A scaling law based on keyhole formation is also utilized in calibrating the parameters of the cylindrical heat source and predicting the challenge experiments. In addition, an improvement on the heat source model is proposed to relate the Volumetric Energy Density (VED_σ) to the melt pool aspect ratio. The model shows further improvement in the prediction of the experimental measurements for the melt pool, including cases at higher VED_σ. Overall, it is concluded that the appropriate selection of laser heat source parameterization scheme along with the heat source model is crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that consider increased physics equations.

    

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3. Thermal and Microstructural Characterization of GRCop-84/In718 Bi-metallic Structures Additively Manufactured by Directed Energy Deposition

   https://doi.org/10.1007/s11837-023-06303-6  

   Wang, Zexiao ; O’Brien, Nicholas ; Jones, Nicholas ; Beuth, Jack ; Shen, Sheng ( December 2023 , JOM)

   As a nickel-based super alloy, Inconel 718 (In718) has gained attention in different industries due to its excellent mechanical behavior under elevated temperatures. Nevertheless, its low thermal conductivity limits its application in many fields, such as thermal energy conversion and heat dissipation. GRCop-84, in contrast, is a copper-based alloy with extremely high thermal conductivity. Making bi-metallic structures with GRCop-84 may expand the thermal-related applications of Inconel 718. In this study, we investigate the thermal properties of In718/GRCop-84 bi-metallic structures fabricated by the directed energy deposition (DED) technique with different process parameters of laser power and scanning velocity. The resulting microstructures were analyzed through scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS), while the frequency-domain thermoreflectance (FDTR) technique has been adopted to acquire the thermal properties. The melt pool thermal conductivities were 50 W/m K on single bead samples and 100 W/m K on single-layer pads, significantly lower than that of bulk GRCop-84. EDS analysis reveals large deviations from standard GRCop-84 compositions inside the melt pool.

    

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4. Multi-Track Melt Pool Width Modeling in Powder Bed Fusion Additive Manufacturing

   https://doi.org/10.1115/1.4063475  

   Wang, Dan ; Chen, Xu ( April 2023 , ASME Letters in Dynamic Systems and Control)

   While powder bed fusion (PBF) additive manufacturing offers many advantages and exciting applications, its broader adoption is hindered by issues with reliability and variations during the manufacturing process. To address this, researchers have identified the importance of using both finite element modeling and control-oriented modeling to predict and improve the quality of printed parts. In this paper, we propose a novel control-oriented multi-track melt pool width model that utilizes the superposition principle to account for the complex thermal interactions that occur during PBF. We validate the effectiveness of the model by applying a finite element model of the thermal fields in PBF.

    

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5. A Numerical Study on the Keyhole Formation During Laser Powder Bed Fusion Process

   https://doi.org/10.1115/1.4044100  

   Shrestha, Subin ; Kevin Chou, Y. ( October 2019 , Journal of Manufacturing Science and Engineering)

   The dynamic phenomenon of a melt pool during the laser powder bed fusion (LPBF) process is complex and sensitive to process parameters. As the energy density input exceeds a certain threshold, a huge vapor depression may form, known as the keyhole. This study focuses on understanding the keyhole behavior and related pore formation during the LPBF process through numerical analysis. For this purpose, a thermo-fluid model with discrete powder particles is developed. The powder distribution, obtained from a discrete element method (DEM), is incorporated into the computational domain to develop a 3D process physics model using flow-3d. The melt pool formation during the conduction mode and the keyhole mode of melting has been discerned and explained. The high energy density leads to the formation of a vapor column and consequently pores under the laser scan track. Further, the keyhole shape resulted from different laser powers and scan speeds is investigated. The numerical results indicated that the keyhole size increases with the increase in the laser power even with the same energy density. The keyhole becomes stable at a higher power, which may reduce the occurrence of pores during laser scanning. 

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