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1. Multi-Objective Process Optimization of Additive Manufacturing: A Case Study on Geometry Accuracy Optimization

   Aboutaleb, A.M.; Bian, L.; Shamsaei, N.; Thompson, S.M.; Rao, P.K. (August 2016, Annual International Solid Freeform Fabrication Symposium)

   Despite recent research efforts improving Additive Manufacturing (AM) systems, quality and reliability of AM built products remains as a challenge. There is a critical need to achieve process parameters optimizing multiple mechanical properties or geometry accuracy measures simultaneously. The challenge is that the optimal value of various objectives may not be achieved concurrently. Most of the existing studies aimed to obtain the optimal process parameters for each objective individually, resulting in duplicate experiments and high costs. In this study we investigated multiple geometry accuracy measures of parts fabricated by Fused Filament Fabrication (FFF) system. An integrated framework for systematically designing experiments is proposed to achieve multiple sets of FFF process parameters resulting in optimal geometry integrity. The proposed method is validated using a real world case study. The results show that optimal properties are achieved in a more efficient manner compared with existing methods. 

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2. Multi-Objective Process Optimization of Additive Manufacturing: A Case Study on Geometry Accuracy Optimization

   Aboutaleb, Amir M.; Bian, L.; Shamsaei, N.; Thompson, S.M.; Rao, Prahalad K. (November 2016, 26th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference)

   Despite recent research efforts improving Additive Manufacturing (AM) systems, quality and reliability of AM built products remains as a challenge. There is a critical need to achieve process parameters optimizing multiple mechanical properties or geometry accuracy measures simultaneously. The challenge is that the optimal value of various objectives may not be achieved concurrently. Most of the existing studies aimed to obtain the optimal process parameters for each objective individually, resulting in duplicate experiments and high costs. In this study we investigated multiple geometry accuracy measures of parts fabricated by Fused Filament Fabrication (FFF) system. An integrated framework for systematically designing experiments is proposed to achieve multiple sets of FFF process parameters resulting in optimal geometry integrity. The proposed method is validated using a real world case study. The results show that optimal properties are achieved in a more efficient manner compared with existing methods. 

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3. Nonlinear observer design for two‐time‐scale systems

   https://doi.org/10.1002/aic.16956  

   Duan, Zhaoyang; Kravaris, Costas (March 2020, AIChE Journal)

   Abstract A two‐time‐scale system involves both fast and slow dynamics. This article studies observer design for general nonlinear two‐time‐scale systems and presents two alternative nonlinear observer design approaches, one full‐order and one reduced‐order. The full‐order observer is designed by following a scheme to systematically select design parameters, so that the fast and slow observer dynamics are assigned to estimate the corresponding system modes. The reduced‐order observer is derived based on a lower dimensional model to reconstruct the slow states, along with the algebraic slow‐motion invariant manifold function to reconstruct the fast states. Through an error analysis, it is shown that the reduced‐order observer is capable of providing accurate estimation of the states for the detailed system with an exponentially decaying estimation error. In the last part of the article, the two proposed observers are designed for an anaerobic digestion process, as an illustrative example to evaluate their performance and convergence properties. 

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4. What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data

   https://doi.org/https://doi.org/10.1016/j.cma.2020.113228  

   Butler, T.; Hakula, H. (October 2020, Computer methods in applied mechanics and engineering)

   null (Ed.)

   In manufacturing processes, controlling system responses with uncertain system inputs, e.g., due to variations in material parameters of critical system sub-components, is a crucial task for performing reliable quality control and verification & validation (V&V) of system design. As a model for a manufacturing process, we consider the production of drums, that is, thin elastic membranes, whose properties are modeled via Dirichlet Laplacian eigenproblems with uncertain diffusion coefficients. Both a quality control and V&V problem are formulated within a data-consistent framework utilizing push-forward and pullback measures. In both problems, the uncertain diffusion coefficients are parameterized for every instance and the corresponding eigen-information defines correlated data streams. Subsequently, the quantities of interest required in the solution to the data-consistent inverse problems are determined by an a posteriori analysis of these data streams using feature extraction techniques. While the methodology proposed here is quite general, the specific efficacy of the proposed methodology is comprehensively explored in the numerical results for both the quality control and V&V problems associated with the manufacturing of drums. 

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5. Dispersed passive RF-sensing for 3D structural health monitoring

   https://doi.org/10.52953/ZQYZ8264  

   Ahmad, Abeer; Sha, Xiao; Athalye, Akshay; Das, Samir R.; Caylor, Kelly; Glisic, Branko; Stanacevic, Milutin; Djuric, Petar M. (January 2022, ITU Journal on Future and Evolving Technologies)

   We propose a sensing system comprising a large network of tiny, battery-less, Radio Frequency (RF)-powered sensors that use backscatter communication. The sensors use an entirely passive technique to 'sense' the parameters of the wireless channel between themselves. Since the material properties influence RF channels, this fine-grain sensing can uncover multiple material properties both at a large scale and fine spatial resolution. In this paper, we study the feasibility of the proposed passive technique for monitoring parameters of material in which the sensors are embedded. We performed a set of experiments where the sensor-to-sensor wireless channel parameters are well-defined using physics-based modeling, and we compared the theoretical and experimentally obtained values. For some material parameters of interest, like humidity or strain, the relationship with the observed wireless channel parameters have to be modeled relying on data-driven approaches. The initial experiments show an observable difference in the sensor-to-sensor channel phase with variation in the applied weights. 

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