Master Thesis: Data-driven Optimal Control of an Additive Manufacturing Process

Siemens Mobility; München; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

Join Siemens Mobility for a unique opportunity to complete your Master’s thesis in automation engineering. Contribute to innovative research in additive manufacturing focusing on data-driven control methods. You'll work alongside engineers and experts, gaining valuable insights into real-world processes while shaping the future of technology.

Qualifikationen

• Currently enrolled in relevant Master's degree program.
• Experience in designing control systems, MATLAB/Simulink, Python, and understanding AI models.
• Good team player with strong analytical skills.

Aufgaben

• Conduct literature review on data-driven control in additive manufacturing.
• Develop an MPC strategy for WAAM processes using LSTM model.
• Gather and analyze data on process parameters.

Kenntnisse

Control systems design

MATLAB/Simulink

Python

AI models understanding

Additive manufacturing knowledge

Git

Analytical thinking

Teamwork

English proficiency

Ausbildung

Master's degree in automation engineering, electronic engineering, mechanical engineering, robotics, physics or related

Jobbeschreibung

Mode of Employment: Limited

Use your knowledge as a springboard.

Do you like the sound of finding the smartest solution side by side with professionals and experts? If so, complete your bachelor’s or master’s thesis with us. We can help you to combine knowledge, discover connections, and formulate ideas. When you join our team, you will gain an insight into a range of departments and processes. It is a chance like no other to break new ground as we head into the future of electrification, automation, and digitalization. Seize this opportunity today!

Change the future with us.

• 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 skills

Make your mark in our exciting world at Siemens.

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As an equal-opportunity employer we are happy to consider applications from individuals with disabilities.

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