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Abstract We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback. 

Abstract 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. 

Abstract Selective laser sintering (SLS) technology produces a substantial amount of un-sintered polyamide 12 powders after the manufacturing process. Failure to recycle and reuse these aged powders not only leads to economic losses but also is environmentally unfriendly. This is particularly problematic for powder particles close to the heat-affected zones that go through severe thermal degradations during the laser sintering processes. Limited procedures exist for systematically reusing such extremely aged powders. This work proposes a systematic method to maximize reusability of aged and extremely aged polyamide 12 powders. Building on a previously untapped interlayer heating, pre-processing, and a systematic mixing of powder materials, we show how reclaimed polyamide 12 powders can be consistently reprinted into functional samples, with mechanical properties even superior to current industrial norms. In particular, the proposed method can yield printed samples with 18.04% higher tensile strength and 55.29% larger elongation at break using as much as 30% of extremely aged powders compared to the benchmark sample. 

Abstract 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. 

This paper presents a least squares formulation and a closed-form solution for identifying dynamical systems using irregular and sparse data obtained by chronologically merging measurements taken by multiple slow sensors of different sampling rates. We provide the theoretical foundation for developing advanced least-squares-based system identification algorithms for cases where the input-output data are asynchronous and/or scarce. Demonstrative examples are provided to validate the proposed method, and indicate the potential of removing the Nyquist sampling limitation in system identification. We provide in details how using 19 percent of the full measurements enables to capture the dynamics of a dynamic system when two slow sensors are collaboratively collecting the system response at different speeds. The required measurements can be further reduced under the proposed collaborative sensing scheme.