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1. 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.

    

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2. New Hammerstein Modeling and Analysis for Controlling Melt Pool Width in Powder Bed Fusion Additive Manufacturing

   https://doi.org/10.1115/1.4050079  

   Wang, Dan ; Zhao, Xinyu ; Chen, Xu ( July 2021 , ASME Letters in Dynamic Systems and Control)

   null (Ed.)

   Abstract Despite the advantages and emerging applications, broader adoption of powder bed fusion (PBF) additive manufacturing is challenged by insufficient reliability and in-process variations. Finite element modeling and control-oriented modeling have been shown to be effective for predicting and engineering part qualities in PBF. This paper first builds a finite element model (FEM) of the thermal fields to look into the convoluted thermal interactions during the PBF process. Using the FEM data, we identify a novel surrogate system model from the laser power to the melt pool width. Linking a linear model with a memoryless nonlinear submodel, we develop a physics-based Hammerstein model that captures the complex spatiotemporal thermomechanical dynamics. We verify the accuracy of the Hammerstein model using the FEM and prove that the linearized model is only a representation of the Hammerstein model around the equilibrium point. Along the way, we conduct the stability and robustness analyses and formalize the Hammerstein model to facilitate the subsequent control designs. 

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3. Control-Oriented Modeling and Repetitive Control in In-Layer and Cross-Layer Thermal Interactions in Selective Laser Sintering

   https://doi.org/10.1115/1.4046367  

   Wang, Dan ; Jiang, Tianyu ; Chen, Xu ( January 2021 , ASME Letters in Dynamic Systems and Control)

   null (Ed.)

   Abstract Although laser-based additive manufacturing (AM) has enabled unprecedented fabrication of complex parts directly from digital models, broader adoption of the technology remains challenged by insufficient reliability and in-process variations. In pursuit of assuring quality in the selective laser sintering (SLS) AM, this paper builds a modeling and control framework of the key thermodynamic interactions between the laser source and the materials to be processed. First, we develop a three-dimensional finite element simulation to understand the important features of the melt pool evolution for designing sensing and feedback algorithms. We explore how the temperature field is affected by hatch spacing and thermal properties that are temperature-dependent. Based on high-performance computer simulation and experimentation, we then validate the existence and effect of periodic disturbances induced by the repetitive in- and cross-layer thermomechanical interactions. From there, we identify the system model from the laser power to the melt pool width and build a repetitive control algorithm to greatly attenuate variations of the melt pool geometry. 

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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%. 

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5. On the Controllability and Observability of Temperature States in Metal Powder Bed Fusion

   https://doi.org/10.1115/1.4056326  

   Wood, Nathaniel ; Hoelzle, David J. ( March 2023 , Journal of Dynamic Systems, Measurement, and Control)

   Abstract Powder bed fusion (PBF) is an additive manufacturing (AM) process 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. Despite its transformational manufacturing capabilities, PBF is currently controlled in the open loop and there is significant demand to apply closed-loop process monitoring and control to the thermal management problem. This paper introduces a controls theoretic analysis of the controllability and observability of temperature states in PBF. The main contributions of the paper are proofs that certain configurations of PBF are classically controllable and observable, but that these configurations are not strongly structurally controllable and observable. These results are complemented by case studies, demonstrating the energy requirement of state estimation under various, industry relevant PBF configurations. These fundamental characterizations of controllability and observability provide a basis for realizing closed-loop PBF temperature estimation. 

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