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1. Autonomous Manufacturing Using Machine Learning: A Computational Case Study With a Limited Manufacturing Budget

   https://doi.org/10.1115/MSEC2020-8472  

   Alam, Md. Ferdous; Shtein, Max; Barton, Kira; Hoelzle, David J. (September 2020, International Manufacturing Science and Engineering Conference)

   null (Ed.)

   Abstract 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. 

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2. Surrogate-based cyber-physical aerodynamic shape optimization of high-rise buildings using wind tunnel testing

   https://doi.org/10.1016/j.jweia.2023.105586  

   Lu, Wei-Ting; Phillips, Brian M.; Jiang, Zhaoshuo (November 2023, Journal of Wind Engineering and Industrial Aerodynamics)

   This study proposes a surrogate-based cyber-physical aerodynamic shape optimization (SB-CP-ASO) approach for high-rise buildings under wind loading. Three components are developed in the SB-CP-ASO procedure: (1) an adaptive subtractive manufacturing technique, (2) a high-throughput wind tunnel testing procedure, and (3) a flexible infilling strategy. The downtime of the procedure is minimized through a parallel manufacturing and testing (llM&T) technique. An unexplored double-section setback strategy with various cross-sections and transitions positions is used to demonstrate the performance of the proposed procedure. A total of 173 physical specimens were evaluated to reach the optimization convergence within the reserved testing window. Further analysis of promising shapes considering multiple design wind speeds is suggested to achieve target performance objectives at various hazard levels. Practical information on setback and cross-section modification strategies is discussed based on the optimization results. In comparison with a square benchmark model, the roof drifts for promising candidates with similar building volumes are reduced by more than 70% at wind speeds higher than 50 m/s. This procedure is expected to provide an efficient platform between owners, architects, and structural engineers to identify ideal candidates within a defined design space for real-world applications of high-rise buildings. 

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3. Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning

   https://doi.org/10.1016/j.matdes.2023.112126  

   Peloquin, Jacob; Kirillova, Alina; Rudin, Cynthia; Brinson, LC; Gall, Ken (June 2023, Materials & Design)

   Advancements in additive manufacturing (AM) technology and three-dimensional (3D) modeling software have enabled the fabrication of parts with combinations of properties that were impossible to achieve with traditional manufacturing techniques. Porous designs such as truss-based and sheet-based lattices have gained much attention in recent years due to their versatility. The multitude of lattice design possibilities, coupled with a growing list of available 3D printing materials, has provided a vast range of 3D printable structures that can be used to achieve desired performance. However, the process of computationally or experimentally evaluating many combinations of base material and lattice design for a given application is impractical. This research proposes a framework for quickly predicting key mechanical properties of 3D printed gyroid lattices using information about the base material and porosity of the structure. Experimental data was gathered to train a simple, interpretable, and accurate kernel ridge regression machine learning model. The performance of the model was then compared to numerical simulation data and demonstrated similar accuracy at a fraction of the computation time. Ultimately, the model development serves as an advancement in ML-driven mechanical property prediction that can be used to guide extension of current and future models. 

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4. Assessing the Manufacturability of Students’ Early-Stage Designs Based on Previous Experience With Traditional Manufacturing and Additive Manufacturing

   https://doi.org/10.1115/1.4063564  

   Pearl, Seth; Meisel, Nicholas A (January 2024, Journal of Mechanical Design)

   Abstract As additive manufacturing (AM) usage increases, designers who wish to maximize AM’s potential must reconsider the traditional manufacturing (TM) axioms they may be more familiar with. While research has previously investigated the potential influences that can affect the designs produced in concept generation, little research has been done explicitly targeting the manufacturability of early-stage concepts and how previous experience and the presenting of priming content in manufacturing affect these concepts. The research in this paper addresses this gap in knowledge, specifically targeting differences in concept generation due to designer experience and presenting design for traditional manufacturing (DFTM) and design for additive manufacturing (DFAM) axioms. To understand how designers approach design creation early in the design process and investigate potential influential factors, participants in this study were asked to complete a design challenge centered on concept generation. Before this design challenge, a randomized subset of these participants received priming content on DFTM and DFAM considerations. These participants’ final designs were evaluated for both traditional manufacturability and additive manufacturability and compared against the final designs produced by participants who did not receive the priming content. Results show that students with low manufacturing experience levels create designs that are more naturally suited for TM. Additionally, as designers’ manufacturing experience levels increase, there is an increase in the number of designs more naturally suited for AM. This correlates with a higher self-reported use of DFAM axioms in the evaluation of these designs. These results suggest that students with high manufacturing experience levels rely on their previous experience when it comes to creating a design for either manufacturing process. Lastly, while the manufacturing priming content significantly influenced the traditional manufacturability of the designs, the priming content did not increase the number of self-reported design for manufacturing (DFM) axioms in the designs. 

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5. Digital Twinning and Optimization of Manufacturing Process Flows

   https://doi.org/10.1115/1.4063234  

   Lee, Hankang; Yang, Hui (November 2023, Journal of Manufacturing Science and Engineering)

   Abstract The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models—job flow graph and AGV traveling graph—to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace. 

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