Master Thesis – The future of laser powder bed fusion: From Fixed Recipes to Adaptive Manufacturing
A Snapshot of Your Day
Imagine developing a new LPBF component without first having to search for or develop the "right" parameter recipe. In this Master Thesis, you will explore how we can move from today's recipe-based manufacturing towards a self-adaptive process where the desired outcome, such as density, geometry, surface quality and productivity, determines how the component should be manufactured.
You will work with real manufacturing and monitoring data and investigate how material properties, geometry, thermal history and in-situ process information can be used to predict manufacturing outcomes and ultimately recommend suitable manufacturing strategies. The work connects additive manufacturing with areas such as Optical Tomography, digital twins, adaptive process control and data-driven manufacturing.
The ambition is bigger than optimizing another parameter set. The thesis asks whether we can eventually remove the need for traditional parameter recipes altogether, enabling a more scalable and adaptive approach to LPBF manufacturing.
How You’ll Make an Impact
- Analyze historical LPBF manufacturing data to understand the complexity and engineering effort associated with today's parameter-recipe approach.
- Develop a feature-based description of the manufacturing process using factors such as geometry, material, thermal history, build position and neighboring-part interaction.
- Explore how Optical Tomography, monitoring data, process settings and geometry information can be used to predict outcomes such as density, porosity, dimensional accuracy, distortion and surface quality
- Develop and evaluate an intent-based recommendation concept that translates quality and productivity requirements into appropriate manufacturing actions and process settings.
- Challenge the developed approach using a new geometry or material and investigate its potential for a first-time-right manufacturing strategy without developing a new parameter recipe.
What You Bring
- You are studying towards a Master's degree in Mechanical Engineering, Additive Manufacturing, Materials Engineering, Production Engineering, Applied Physics, Data Science, Machine Learning or a related field.
- You are curious about the intersection between physical manufacturing processes, engineering and data-driven methods, and want to solve problems with real industrial relevance.
- You have an analytical mindset and are comfortable working with complex datasets; experience with Python, MATLAB or similar data-analysis tools is beneficial.
- Knowledge of LPBF/additive manufacturing, process monitoring, machine learning, digital twins or manufacturing simulation is an advantage, but we value curiosity and the ability to learn just as highly.
- You enjoy taking an open research question, structuring it into testable hypotheses and translating the results into practical engineering solutions.
- You are comfortable working collaboratively with specialists from different technical disciplines while also driving your own thesis work independently.
About the Team
You will join an Additive Manufacturing environment working at the intersection of design, materials, manufacturing, process development and digitalization. The thesis connects directly with ongoing technology areas including Beacon Build, Optical Tomography fingerprinting, OT Predictive and digital twin initiatives, Smart Fusion, automated qualification and future adaptive LPBF systems. You will have the opportunity to work closely with experienced AM specialists and explore a research question with direct industrial relevance: How can we teach the manufacturing system what outcome we want, instead of telling it which parameter recipe to use?
Our Gas Services division offers Low-emission power generation through service and decarbonization. Zero or low emission power generation and all gas turbines under one roof, steam turbines and generators. Decarbonization opportunities through service offerings, modernization, and digitalization of the fleet.
We can offer you employment benefits such as: reduction of working hours, advance vacation, health care allowance and an eventual possibility to a flexible working place.
Who is Siemens Energy?
At Siemens Energy, we are more than just an energy technology company. With ~100,000 dedicated employees in more than 90 countries, we develop the energy systems of the future, ensuring that the growing energy demand of the global community is met reliably and sustainably. The technologies created in our research departments and factories drive the energy transition and provide the base for one sixth of the world's electricity generation.
Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.
Find out how you can make a difference at Siemens Energy: https://www.siemens-energy.com/employeevideo
Our Commitment to Diversity
Lucky for us, we are not all the same. Through diversity, we generate power. We run on inclusion and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character – no matter what ethnic background, gender, age, religion, identity, or disability. We energize society, all of society, and we do not discriminate based on our differences.
Application
Don’t hesitate – apply via https://siemensenergy.avature.net/internalcareers, id nr 304989 not later than 2026-11-17. Ongoing selection is applied; the role might be filled before the last application date.
Trade Union Representatives:
Unionen: [email protected]
Sveriges Ingenjörer & SACO: [email protected]
Ledarna: Anders Fors, [email protected]
IF Metall: Mikael Malmgren, [email protected]
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