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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. 

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%. 

Powder Bed Fusion (PBF) faces ongoing challenges in the areas of process monitoring and control. Standard methods for alleviating these issues rely on machine learning, which requires costly and time-consuming training data. Expense is compounded by the perceived necessity of using sensors with extremely high resolutions. This research avoids this cost by employing an Ensemble Kalman Filter (EnKF), which uses measured data to correct physics-based model predictions of the process, to monitor part internal temperature fields during building. This work tests EnKF performance, in simulation, for two model architectures, using simulated cameras of varying resolution as our measuring instruments. Crucially, we show that increasing camera resolution produces diminishing returns in EnKF accuracy, relative to the model predictions, with up to 81% error reduction. This result shows that current AM quality control practices with expensive sensors may be inefficient; with appropriate algorithms, cheaper setups may be used with little additional error. 

This paper studies the concept of manufacturing systems that autonomously learn how to build parts to a user-specified performance. To perform such a function, these manufacturing systems need to be adaptable to continually change their process or design parameters based on new data, have inline performance sensing to generate data, and have a cognition element to learn the correct process or design parameters to achieve the specified performance. Here, we study the cognition element, investigating a panel of supervised and reinforcement learning machine learning algorithms on a computational emulation of a manufacturing process, focusing on machine learning algorithms that perform well under a limited manufacturing, thus data generation, budget. The case manufacturing study is for the manufacture of an acoustic metamaterial and performance is defined by a metric of conformity with a desired acoustic transmission spectra. We find that offline supervised learning algorithms, which dominate the machine learning community, require an infeasible number of manufacturing observations to suitably optimize the manufacturing process. Online algorithms, which continually modify the parameter search space to focus in on favorable parameter sets, show the potential to optimize a manufacturing process under a considerably smaller manufacturing budget.