Thermal Modeling Of Additive Manufacturing Using Graph Theory
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Thermal Modeling of Additive Manufacturing Using Graph Theory
Metal additive manufacturing (AM/3D printing) offers unparalleled advantages over conventional manufacturing, including greater design freedom and a lower lead time. However, the use of AM parts in safety-critical industries, such as aerospace and biomedical, is limited by the tendency of the process to create flaws that can lead to sudden failure during use. The root cause of flaw formation in metal AM parts, such as porosity and deformation, is linked to the temperature inside the part during the process, called the thermal history. The thermal history is a function of the process parameters and part design. Consequently, the first step towards ensuring consistent part quality in metal AM is to understand how and why the process parameters and part geometry influence the thermal history. Given the current lack of scientific insight into the causal design-process-thermal physics link that governs part quality, AM practitioners resort to expensive and timeconsuming trial-and-error tests to optimize part geometry and process parameters. An approach to reduce extensive empirical testing is to identify the viable process parameters and part geometry combinations through rapid thermal simulations. However, a major barrier that deters physics-based design and process optimization efforts in AM is the prohibitive computational burden of existing finite element-based thermal modeling. The objective of this thesis is to understand the causal effect of process parameters on the temperature distribution in AM parts using the theory of heat dissipation on graphs (graph theory). We develop and apply a novel graph theory-based computational thermal modeling approach for predicting the thermal history of titanium alloy parts made using the directed energy deposition metal AM process. As an example of the results obtained for one of the three test parts studied in this work, the temperature trends predicted by the graph theory approach had error ~11% compared to experimental trends. Moreover, the graph theory simulation was obtained within 9 minutes, which is less than the 25 minutes required to print the part.
Modeling, Analysis, and Control of 3D Printing Processes
Three-dimensional (3D) printing, also known as additive manufacturing, revolutionizes modern manufacturing by enabling rapid, customized, and complex part fabrication across various industries. To ensure consistent product quality there is a need for advanced techniques in modeling, analysis, and control of 3D printing processes. Modeling helps in understanding the intricate physical phenomena involved, like heat transfer, material flow, and phase changes, while analytical methods predict outcomes and identify defects. Control systems minimize errors and ensure process stability. Further exploration into this field may improve reliability, efficiency, and scalability in 3D printing technologies. Modeling, Analysis, and Control of 3D Printing Processes explores the key aspects involved in the modeling, analysis, and control of 3D printing processes. It examines modeling, simulation, analysis, and control mechanisms, including the intricacies of the printing process, and analyzes the associated challenges, implementing effective control strategies for advanced 3D printing. This book covers topics such as circular economy, material recycling, and sensor technologies, and is a useful resource for engineers, business owners, manufacturers, academicians, researchers, and scientists.
Machine Learning for Powder-Based Metal Additive Manufacturing
Machine Learning for Powder-based Metal Additive Manufacturing outlines machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs. The book combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications. The book covers ML for design in AM, ML for materials development and intelligent monitoring in metal AM, both geometrical deviation and physics informed machine learning modeling, as well as data-driven cost estimation by ML.In addition, optimization for slicing and orientation, ML to create models of materials for AM processes, ML prediction for better mechanical and microstructure prediction, and feature extraction by sensing data are all covered, and each chapter includes a case study. - Covers machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs - Combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications - Discusses algorithm development of ML for metal AM, metal AM process modeling and optimization, mathematical and simulation studies of metal AM, and pre- and post-processing smart methods for metal AM