Aerospace Engineering Commons^™

Quantitative Assessment And Characterization Of Tool Wear Phenomena In Advanced Manufacturing Processes, Oybek Valijonovich Tuyboyov

Technical science and innovation

This paper explores the quantitative assessment and characterization of tool wear phenomena in advanced manufacturing processes, employing a multifaceted approach encompassing traditional measurements, image processing, machine learning, and predictive modeling. The study emphasizes the intricate dynamics of tool wear and its direct impact on cutting tool performance, addressing challenges in real-time monitoring and optimization of machining operations. Traditional methods like VBmax measurement are juxtaposed with advanced techniques such as the improved conditional generative adversarial net with a high-quality optimization algorithm (CGAN-HQOA), efficient channel attention destruction and construction learning (ECADCL), and shape descriptors based on contour, moments, orientations, and texture. Artificial …

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

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 …

Advanced Ensemble Modeling Method For Space Object State Prediction Accounting For Uncertainty In Atmospheric Density, Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta

Mechanical and Aerospace Engineering Faculty Research & Creative Works

For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space traffic management activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainties, which are modeled using probabilistic machine learning techniques. The recently proposed "HASDM-ML," "CHAMP-ML," and "MSIS-UQ" machine learning models for density estimation (Licata and Mehta, 2022b; Licata et al., 2022b) are used in this work. The investigation is convoluted because of the spatial and temporal correlation of the atmospheric density …

Additive Manufacturing Of Complexly Shaped Sic With High Density Via Extrusion-Based Technique – Effects Of Slurry Thixotropic Behavior And 3d Printing Parameters, Ruoyu Chen, Adam Bratten, Joshua Rittenhouse, Tian Huang, Wenbao Jia, Ming-Chuan Leu, Haiming Wen

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additive manufacturing of dense SiC parts was achieved via an extrusion-based process followed by electrical-field assisted pressure-less sintering. The aim of this research was to study the effect of the rheological behavior of SiC slurry on the printing process and quality, as well as the influence of 3D printing parameters on the dimensions of the extruded filament, which are directly related to the printing precision and quality. Different solid contents and dispersant- Darvan 821A concentrations were studied to optimize the viscosity, thixotropy and sedimentation rate of the slurry. The optimal slurry was composed of 77.5 wt% SiC, Y2O3 and Al2O3 …

Predicting Defects In Laser Powder Bed Fusion Using In-Situ Thermal Imaging Data And Machine Learning, Sina Malakpour Estalaki, Cody S. Lough, Robert G. Landers, Edward C. Kinzel, Tengfei Luo

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

Procase: A Prototype Of Intelligent Case-Based Process Planning System With Simulation Environment, Hao Yang, Wen F. Lu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

An intelligent case-based process planning system with interactive graphic simulation environment, PROCASE, is developed lo demonstrate an integrated methodology of case-based process planning system. In PROCASE, both the mechanical part features and the machining operations are represented with a frame-based scheme. PROCASE contains a retriever, a modifier, a simulator and a repairer. It distinguishes itself from traditional rule-based process planning systems by representing the process planning knowledge through previous process planning cases instead of production rules. It therefore can overcome some problems in the traditional rule-based expert systems. PROCASE currently resides in IRIS Indigo workstation. With a user-friendly graphic environment, …