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Comprehensive modeling of process-molten pool condition-property correlations for wire-feed laser additive manufacturing
Jamnikar, Noopur
Jamnikar, Noopur
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2021
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Abstract
Wire-feed laser additive manufacturing (WLAM) is gaining wide interest due to its high level of automation, high deposition rates, and good quality of printed parts. The complexity of modeling the directed energy deposition (DED) process, high characterization and printing cost, and the destructive testing of the final build part for quality testing motivates the need for developing in situ quality assurance and control techniques. In-process monitoring and feedback controls that would reduce the uncertainty in the quality of the fabricated parts are in the early stages of development. Machine learning (ML) promises the ability to accelerate the adoption of in-process monitoring and control in additive manufacturing (AM) by modeling and predicting process-sensing-property connections between process setting inputs and material quality outcomes. However, the lack of sufficient sensing and characterization data for training ML models is a significant challenge in the field of AM industry due to associated high costs. This thesis explores the in situ quality assurance and control methods by studying the process-molten pool condition-property relation for the robotic laser wire-feed DED process. Analysis and characterization are performed on the experimentally collected in situ sensing data for the molten pool under a set of controlled process parameters for a WLAM system. The real-time molten pool dimensional information and temperature data are the indicators for achieving good quality of the build, which can be directly controlled by processing parameters. Thus, the process-molten pool condition-property relations are of preliminary importance for developing a quality control and assurance framework. The results highlight collaborative and quantitative multi-modality models for controlling and estimating the process and quality parameters using real-time sensing data.
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