More Like this

1. ‘Seeing’ the Temperature Inside the Part during the Powder Bed Fusion Process

   Wood, N. ; Hoelzle, D. ( August 2019 , Proceedings of the 2019 Annual International Solid Freeform Fabrication Symposium)

   Powder Bed Fusion (PBF) is a type of Additive Manufacturing (AM) technology that builds parts in a layer-by-layer fashion out of a bed of metal powder via the selective melting action of a laser or electron beam heat source. The technology has become widespread, however the demand is growing for closed loop process monitoring and control in PBF systems to replace the open loop architectures that exist today. This paper demonstrates the simulated efficacy of applying closed-loop state estimation to the problem of monitoring temperature fields within parts during the PBF build process. A simplified LTI model of PBF thermal physics with the properties of stability, controllability and observability is presented. An Ensemble Kalman Filter is applied to the model. The accuracy of this filters’ predictions are assessed in simulation studies of the temperature evolution of various test parts when subjected to simulated laser heat input. The significant result of this study is that the filter supplied predictions that were about 2.5x more accurate than the open loop model in these simulation studies. 

   more » « less
2. Closed-Loop High-Fidelity Simulation Integrating Finite Element Modeling With Feedback Controls in Additive Manufacturing

   https://doi.org/10.1115/1.4048364  

   Wang, Dan ; Chen, Xu ( February 2021 , Journal of Dynamic Systems, Measurement, and Control)

   null (Ed.)

   Abstract A high-precision additive manufacturing (AM) process, powder bed fusion (PBF) has enabled unmatched agile manufacturing of a wide range of products from engine components to medical implants. While finite element modeling and closed-loop control have been identified key for predicting and engineering part qualities in PBF, existing results in each realm are developed in opposite computational architectures wildly different in time scale. This paper builds a first-instance closed-loop simulation framework by integrating high-fidelity finite element modeling with feedback controls originally developed for general mechatronics systems. By utilizing the output signals (e.g., melt pool width) retrieved from the finite element model (FEM) to update directly the control signals (e.g., laser power) sent to the model, the proposed closed-loop framework enables testing the limits of advanced controls in PBF and surveying the parameter space fully to generate more predictable part qualities. Along the course of formulating the framework, we verify the FEM by comparing its results with experimental and analytical solutions and then use the FEM to understand the melt-pool evolution induced by the in- and cross-layer thermomechanical interactions. From there, we build a repetitive control (RC) algorithm to attenuate variations of the melt pool width. 

   more » « less
3. Closed-Loop Simulation Integrating Finite Element Modeling With Feedback Controls in Powder Bed Fusion Additive Manufacturing

   https://doi.org/10.1115/ISFA2020-9611  

   Wang, Dan ; Chen, Xu ( September 2020 , Proceedings of ASME/ISCIE International Symposium on Flexible Automation)

   null (Ed.)

   Powder bed fusion (PBF) additive manufacturing has enabled unmatched agile manufacturing of a wide range of products from engine components to medical implants. While high-fidelity finite element modeling and feedback control have been identified key for predicting and engineering part qualities in PBF, existing results in each realm are developed in opposite computational architectures wildly different in time scale. Integrating both realms, this paper builds a first-instance closed-loop simulation framework by utilizing the output signals retrieved from the finite element model (FEM) to directly update the control signals sent to the model. The proposed closed-loop simulation enables testing the limits of advanced controls in PBF and surveying the parameter space fully to generate more predictable part qualities. Along the course of formulating the framework, we verify the FEM by comparing its results with experimental and analytical solutions and then use the FEM to understand the melt-pool evolution induced by the in-layer thermomechanical interactions. From there, we build a repetitive control algorithm to greatly attenuate variations of the melt pool width.

    

   more » « less
4. The Ensemble Kalman Filter as a tool for estimating temperatures in the powder bed fusion process

   https://doi.org/10.23919/ACC50511.2021.9482856  

   Wood, Nathaniel ; Schwalbach, Edwin ; Gillman, Andrew ; Hoelzle, David J. ( May 2021 , 2021 American Control Conference (ACC))

   Powder Bed Fusion (PBF) is a type of additive manufacturing process that builds parts out of metal powder in a layerwise fashion. Quality control (QC) remains an unsolved problem for PBF. Data-driven models of PBF are expensive to train and maintain, in terms of materials and machine time, because they are sensitive to changes in processing conditions.The length and time scale discrepancies of the process make physics-based modeling impractical to implement. We propose monitoring PBF with an Ensemble Kalman Filter (EnKF). The EnKF combines the computational efficiency of datadriven models with the flexibility of physics-based models, while mitigating the flaws of either method. We validate EnKF performance for linear process models, using finite element method data in place of measured experimental data. We show that the EnKF can reduce the error signal 2-norm and 1-norm relative to the open loop model by as much as 75%. 

   more » « less
5. Physics-Guided Long Short-Term Memory Networks for Emission Prediction in Laser Powder Bed Fusion

   https://doi.org/10.1115/1.4063270  

   Lei, Rong ; Guo, Y B ; Guo, Weihong “Grace” ( January 2024 , Journal of Manufacturing Science and Engineering)

   Powder bed fusion (PBF) is an additive manufacturing process in which laser heat liquefies blown powder particles on top of a powder bed, and cooling solidifies the melted powder particles. During this process, the laser beam heat interacts with the powder causing thermal emission and affecting the melt pool. This paper aims to predict heat emission in PBF by harnessing the strengths of recurrent neural networks. Long short-term memory (LSTM) networks are developed to learn from sequential data (emission readings), while the learning is guided by process physics including laser power, laser speed, layer number, and scanning patterns. To reduce the computational efforts on model training, the LSTM models are integrated with a new approach for down-sampling the pyrometry raw data and extracting useful statistical features from raw data. The structure and hyperparameters of the LSTM model reflect several iterations of tuning based on the training on the pyrometer readings data. Results reveal useful knowledge on how raw pyrometer data should be processed to work the best with LSTM, how physics features are informative in predicting overheating, and the effectiveness of physics-guided LSTM in emission prediction.

    

   more » « less