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    An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques by Taoming Chen, Chun Li, Zhexiang Zou, Qi Han, Bing Li, Fengshou Gu, Andrew D. Ball

    Published 2024-11-01
    “…By employing range and regression analyses methods, this study quantitatively evaluates the interdependencies among cutting parameters, forces, temperatures, and roughness, subsequently formulating a multivariate regression model to predict surface roughness. Finally, a turning experiment under actual working conditions was conducted, confirming the model’s capacity to predict the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>a</mi></mrow></msub></mrow></semantics></math></inline-formula> trend with an accuracy of 85.07%. …”
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    Advancing the Prediction and Evaluation of Blast-Induced Ground Vibration Using Deep Ensemble Learning with Uncertainty Assessment by Sinem Bozkurt Keser, Mahmut Yavuz, Gamze Erdogan Erten

    Published 2025-05-01
    “…This study aims to propose a deep ensemble model to predict the blast-induced ground vibration and quantify the prediction uncertainty, which is usually not addressed. …”
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    Deep Learning Framework for Predicting Transonic Wing Buffet Loads Due to Structural Eigenmode-Based Deformations by Rebecca Zahn, Moritz Zieher, Christian Breitsamter

    Published 2025-05-01
    “…Comparing the pressure loads modeled by the hybrid ROM and the reference full-order numerical solution, an overall good prediction performance is indicated with mean squared error (MSE) values mostly below <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, reaching local maxima of about <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>12</mn><mo>%</mo></mrow></semantics></math></inline-formula>, due to strong pressure gradients associated with pronounced shock oscillations.…”
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