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Conduct a literature review on data-driven control and optimization methods in additive manufacturing, specifically focusing on Wire Arc Additive Manufacturing (WAAM) processes Define the specific gaps in current WAAM process control research, particularly in data-driven control approaches (e.g., the lack of robust models, challenges in control of melt pool heat, layer height consistency) Gather data on process parameters and outputs from experiments Use the collected data to train an LSTM model that accurately represents the WAAM process dynamics for use in a Model Predictive Control (MPC) framework Develop an MPC strategy for the WAAM process, using the LSTM plant model as the basis for predictive control Assess model performance You will work within a team which includes engineers, students, developers as well as experts for the manufacturing processes The duration of the Master’s Thesis is based on university regulations (usually a contract with duration of 6 months is issued)What you need to make real what matters.
You are currently enrolled in a master’s degree program in automation engineering, electronic engineering, mechanical engineering, robotics, physics or a related subject You have experience in designing control systems in MATLAB/Simulink You have experience in a programming language like Python You have some understanding of AI models Good knowledge of additive manufacturing and git is an advantage Personally, you impress us with a high level of initiative, analytical thinking as well as a structured way of working in a team environment You have good English skillsMake your mark in our exciting world at Siemens.
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