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  1. 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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    Free, publicly-accessible full text available July 1, 2024
  2. Three-dimensional (3D) printing is implemented for surface modification of titanium alloy substrates with multilayered biofunctional polymeric coatings. Poly(lactic-co- glycolic) acid (PLGA) and polycaprolactone (PCL) polymers were embedded with amorphous calcium phosphate (ACP) and vancomycin (VA) therapeutic agents to promote osseointegration and antibacterial activity, respectively. PCL coatings revealed a uniform deposition pattern of the ACP-laden formulation and enhanced cell adhesion on the titanium alloy substrates as compared to the PLGA coatings. Scanning electron microscopy and Fourier-transform infrared spectroscopy confirmed a nanocomposite structure of ACP particles showing strong binding with the polymers. Cell viability data showed comparable MC3T3 osteoblast proliferation on polymeric coatings as equivalent to positive controls. In vitro live/dead assessment indicated higher cell attachments for 10 layers (burst release of ACP) as compared to 20 layers (steady release) for PCL coatings. The PCL coatings loaded with the antibacterial drug VA displayed a tunable release kinetics profile based on the multilayered design and drug content of the coatings. Moreover, the concentration of active VA released from the coatings was above the minimum inhibitory concentration and minimum bactericidal concentration, demonstrating its effectiveness against Staphylococcus aureus bacterial strain. This research provides a basis for developing antibacterial biocompatible coatings to promote osseointegration of orthopedic implants. 
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  3. K. Ellis, W. Ferrell (Ed.)
    Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin. 
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  4. The development of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) has influenced Cyber-Physical Manufacturing Systems (CPMS). Collaborative manufacturing among organizations with geographically distributed operations using Nanomanufacturing (NM) requires integrated networking for enhanced productivity. The present research provides a unique cyber nanomanufacturing framework by combining digital design with various artificial neural networks (ANN) approaches to predict the optimal nano/micro-manufacturing process. It enables the visualization tool for real-time allocation of nano/micro-manufacturing resources to simulate machine availability for five types of NM processes in real-time for a dynamic machine identification system. This research establishes a foundation for a smart agent system with predictive capabilities for cyber nanomanufacturing in real-time. 
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