HIGH FIDELITY ADDITIVE MANUFACTURING PROCESS MODELING FOR RESIDUAL STRESS AND DEFORMATION
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The ever increasing demand for high speed transport is causing a massive increase in the amount of passengers utilizing air travel. To be able to meet the ever increasing regulatory demands for reduced emissions that are being placed on the air travel industry, there exists an ever present need to advance their technology at an incredible pace. This leads to development and use of the most cutting edge and advanced technologies available, such as Topology Optimization and Additive Manufacturing. Challenges still remain in being able to effectively utilize these technologies as they are both relatively new, however integrating the two together has massive potential for the air travel industry to meet and exceed the emissions regulations. This work presents the methodology for developing a high fidelity mesoscale simulation of the additive manufacturing process which considers all aspects of the machines and the process parameters involved with the construction of components, including the effects of powder bed, Gaussian laser distributions, and specific laser movements. The methodology utilizes the element birth and death finite element formulation to simulate the addition of layers over time and also performs a full transient thermo-mechanical simulation to capture both the temperature history related to the moving heat source, as well as the effect of the temperature history on the residual stress and deformation that is inherent in components generated through additive manufacturing. The methodology was validated by comparing in-situ temperature measurements to the simulation data and was deemed acceptable by achieving 15% error throughout the process. The final stage of this work involved a parameter analysis to capture the effects of the parameters on the residual stress and deformation. Results showed agreement with other literature results, and provided trends that shed some light on the uncertainty of the AM process in hopes of providing recommendations for the development of a methodology for large scale process modeling.