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Full-Text Articles in Aerospace Engineering
Experimental, Computational, And Machine Learning Methods For Prediction Of Residual Stresses In Laser Additive Manufacturing: A Critical Review, Sung Heng Wu, Usman Tariq, Ranjit Joy, Todd Sparks, Aaron Flood, Frank W. Liou
Experimental, Computational, And Machine Learning Methods For Prediction Of Residual Stresses In Laser Additive Manufacturing: A Critical Review, Sung Heng Wu, Usman Tariq, Ranjit Joy, Todd Sparks, Aaron Flood, Frank W. Liou
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions …
Optimal Tilt-Wing Evtol Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks, Shuan Tai Yeh, Xiaosong Du
Optimal Tilt-Wing Evtol Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks, Shuan Tai Yeh, Xiaosong Du
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of …