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1. Future of Non-Destructive Testing in the Era of Additive Manufacturing and Machine Learning

   Gupta, N (March 2024, Journal of Non-Destructive Testing and Evaluation)

   Additive manufacturing (AM) methods have become mainstream in many industry sectors, especially aeronautics and space structures, where production volume for components is low and designs are highly customized. The frequency of launching space missions is increasing around the world. Some of these missions are sending landers and rovers to moon, mars, and other planets. Such space structures require numerous parts that are unique in design or are produced in just one or a very small production run. Such parts produced for high stake and very expensive missions require complete confidence in the quality of each part. Characterization of parts manufactured by AM is a significant challenge for many existing methods due to the geometric complexity, feature size in the structure, and size of the part. This paper discusses various challenges in applying current characterization methods to the AM sector. Machine learning (ML) methods are considered promising in materials and manufacturing fields. However, generating the training dataset by creating a large number of parts is expensive and impractical. New methods are required to train the ML algorithms on small datasets, especially for parts of unique geometry that are produced in limited production run such as space structures. 

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2. A facile multi-material direct laser writing strategy

   https://doi.org/10.1039/c9lc00398c  

   Lamont, Andrew C.; Restaino, Michael A.; Kim, Matthew J.; Sochol, Ryan D. (July 2019, Lab on a Chip)

   Direct laser writing (DLW) is a three-dimensional (3D) manufacturing technology that offers vast architectural control at submicron scales, yet remains limited in cases that demand microstructures comprising more than one material. Here we present an accessible microfluidic multi-material DLW (μFMM-DLW) strategy that enables 3D nanostructured components to be printed with average material registration accuracies of 100 ± 70 nm (Δ X ) and 190 ± 170 nm (Δ Y ) – a significant improvement versus conventional multi-material DLW methods. Results for printing 3D microstructures with up to five materials suggest that μFMM-DLW can be utilized in applications that demand geometrically complex, multi-material microsystems, such as for photonics, meta-materials, and 3D cell biology. 

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3. Grid Search Hyperparameter Tuning in Additive Manufacturing Processes

   Ogunsanya, M.; Isichei, J.; Desai, S. (July 2023, Manufacturing letters)

   In Machine learning (ML) and deep learning (DL), hyperparameter tuning is the process of selecting the combination of optimal hyperparameters that give the best performance. Thus, the behavior of some machine learning (ML) and deep learning (DL) algorithms largely depend on their hyperparameters. While there has been a rapid growth in the application of machine learning (ML) and deep learning (DL) algorithms to Additive manufacturing (AM) techniques, little to no attention has been paid to carefully selecting and optimizing the hyperparameters of these algorithms in order to investigate their influence and achieve the best possible model performance. In this work, we demonstrate the effect of a grid search hyperparameter tuning technique on a Multilayer perceptron (MLP) model using datasets obtained from a Fused Filament Fabrication (FFF) AM process. The FFF dataset was extracted from the MakerBot MethodX 3D printer using internet of things (IoT) sensors. Three (3) hyperparameters were considered – the number of neurons in the hidden layer, learning rate, and the number of epochs. In addition, two different train-to-test ratios were considered to investigate their effects on the AM process data. The dataset consisted of five (5) dominant input parameters which include layer thickness, build orientation, extrusion temperature, building temperature, and print speed and three (3) output parameters: dimension accuracy, porosity, and tensile strength. RMSE, and the computational time, CT, were both selected as the hyperparameter performance metrics. The experimental results reveal the optimal configuration of hyperparameters that contributed to the best performance of the MLP model. 

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4. Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

   https://doi.org/10.1115/1.4046293  

   López, Christian E.; Cunningham, James; Ashour, Omar; Tucker, Conrad S. (October 2020, Journal of Computing and Information Science in Engineering)

   Abstract This work presents a deep reinforcement learning (DRL) approach for procedural content generation (PCG) to automatically generate three-dimensional (3D) virtual environments that users can interact with. The primary objective of PCG methods is to algorithmically generate new content in order to improve user experience. Researchers have started exploring the use of machine learning (ML) methods to generate content. However, these approaches frequently implement supervised ML algorithms that require initial datasets to train their generative models. In contrast, RL algorithms do not require training data to be collected a priori since they take advantage of simulation to train their models. Considering the advantages of RL algorithms, this work presents a method that generates new 3D virtual environments by training an RL agent using a 3D simulation platform. This work extends the authors’ previous work and presents the results of a case study that supports the capability of the proposed method to generate new 3D virtual environments. The ability to automatically generate new content has the potential to maintain users’ engagement in a wide variety of applications such as virtual reality applications for education and training, and engineering conceptual design. 

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5. Toward Geometric Control of Late-Stage Diffusion Properties for 3D Printed Biodegradable Microstructures

   https://doi.org/10.1109/MEMS51782.2021.9375465  

   Freeman, Emmett Z.; Grosvenor, Eleanor C.; Rosenthal, Ian B.; Acevedo, Ruben; Sochol, Ryan D. (January 2021, 2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS))

   null (Ed.)

   The ability to manufacture biodegradable structures at small scales is integral to a variety of applications in biological, medical, and pharmaceutical fields. Recent developments in additive manufacturing (or "three-dimensional (3D) printing") allow for biodegradable materials to be printed with high resolution; however, there is typically a limit with respect to a resolvable feature size (e.g., layer height) that dictates the minimum increments for tuning distinct degradation-mediated functionalities via print geometry. Here we investigate the potential to 3D print designs that afford additional degrees of control during intermediate stages between the complete biodegradation of microstructures that differ by a single layer height. Preliminary fabrication results revealed effective printing of tubular 3D biodegradable gelatin methacryloyl (GelMA) structures with outer diameters of 100 μm and wall thicknesses of 35 μm using two-photon direct laser writing (DLW)-based additive manufacturing. Simulation results for varying designs suggest that both the total degradation time as well as the diffusion dynamics through a microstructure during the final stage of biodegradation can be modulated via geometric means. Thus, the concepts presented in this work could open new avenues in areas including drug delivery and biomaterials. 

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