SWERIM AB jobb

Project Description Powder Bed Fusion by Electron Beam (PBF-EB) normally aims to produce components with high density. However, productivity may be improved by creating only a gas-tight outer shell around a sintered or semi-melted porous core, followed by Hot Isostatic Pressing (HIP). In PBF-EB, internal pores are formed under vacuum, which is favorable for pore closure during HIP provided that the shell remains gas-tight. Previous studies in both PBF-EB and PBF-LB have demonstrated the feasibility of shell-core concepts, although validation for more complex geometries remains limited.[1]­,[2] If HIP is already required, the approach reduces energy input, build time, cost, and evaporation of sensitive alloying elements. The key challenges are to identify stable process parameters, verify shell integrity, and quantify porosity, densification, and dimensional shrinkage.   This master thesis will focus primarily on experimental work in the PBF-EB printer. The student will evaluate a limited set of process parameters to produce test parts with a dense shell and a porous sintered or semi-melted core. Samples will be characterized before and after HIP to determine process stability, shell integrity, density, pore morphology, microstructure, and dimensional change. The work will generate an initial dataset for shrinkage compensation and include a limited finite element modelling (FEM) task to predict and compensate shrinkage.   The aim is to determine whether a shell-core PBF-EB strategy can shorten build time while producing preforms suitable for HIP consolidation, and to establish an initial method for compensating predictable shrinkage. The scope is sized for one 30-credit master thesis: one material, simple coupon geometries, a few iterative test prints, 1-2 HIP cycles, basic sample characterization, FEM shrinkage modelling, and printing a more complex shape to evaluate the developed model and process parameters. Full constitutive model development, extensive mechanical qualification, and industrial-scale validation are outside the thesis scope. Scope and Objectives The student will:Review PBF-EB shell-core strategies, HIP densification, shrinkage compensation and relevant FEM approaches. Define a feasible specimen design with an airtight shell and a sintered and/or semi-melted core. Screen a small set of printer variables. Produce a dense reference condition and a number of shell-core conditions with varying initial density levels. Characterise shell integrity, density, pore morphology, dimensions, and microstructure before HIP. HIP and quantify densification and dimensional shrinkage. Create an initial shrinkage-compensation relationship based on FEM modelling. Estimate potential build-time and cost benefits relative to the dense reference using recorded machine data. Expected outcomesA documented PBF-EB process window for manufacturing airtight shell-core structures with porous interiors. A dataset containing process parameters, density measurements, pore characteristics, shell integrity assessments, 3D scan dimensional measurements before and after HIP. Quantification of HIP-induced densification, shrinkage, and geometric deviation for selected shell-core conditions. A simplified FEM-based workflow for shrinkage prediction calibrated using experimental results. Validation of the methodology on a more complex geometry representative of practical applications. An optional comparison between PBF-EB and PBF-LB shell-core structures. An indicative assessment of productivity and cost benefits compared with conventional dense PBF-EB manufacturing. Recommendations for future work involving more complex geometries, wider process windows, and more advanced modelling approaches. The student will refine the experimental matrix and FEM together with supervisors and colleagues at Swerim. A successful thesis is expected to prioritize robust PBF-EB processing, verification of shell airtightness, reliable HIP densification, and accurate shrinkage measurements. The FEM work will focus on practical shrinkage prediction and model calibration. Comparison with PBF-LB material is considered a stretch objective and will be included only if resources and project timing permit. Qualifications Master’s student in materials science, mechanical, manufacturing, aerospace engineering, applied physics, or a related field. A strong understanding of physical metallurgy and an interest in hands-on experimental work, systematic data analysis, and problem-solving are essential. Access to FEM software such as Abaqus or COMSOL through the university is required because Swerim has limited number of software licenses. Practical experience with metal powder bed fusion (PBF-EB/LB), metallurgical lab preparation, microscopy, finite element modeling (FEM), metrology, or Python/MATLAB is advantageous but not required. Project time The project is intended for a master thesis (30 credits). The planned start is January 2027, or another date agreed with the selected student. Further information This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp). For further information please contact: Emil Strandh, [email protected] Application Apply by using the application function below. The application can be written in English or Swedish. Latest date for application is 20th of November. You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means we can fill the position before the deadline. [1] https://doi.org/10.3390/ma16165510 [2] Whitepaper | HIP for Free: Speed Printing Unleashed

Project Description: This master's thesis is conducted as part of a collaboration between Swerim and Sandvik Coromant. The project aims to develop machine learning methods for automated and accelerated analysis of X-ray diffraction (XRD) data from a wide range of cemented carbide grades and PVD/CVD coatings engineered for cutting tool applications.  The project will focus on generating a database of simulated diffraction patterns and using it to train models that can automatically identify the correct phase constituents, perform peak fitting, and extract detailed microstructural information such as phase fractions crystallographic texture, microstrain, and other relevant material parameters. The objective is to significantly reduce the time required for phase identification, peak fitting, and comprehensive diffraction analysis compared to conventional workflows. The work will evaluate machine learning approaches both as a complement to and as a potential alternative for traditional Rietveld refinement, contributing to the development of next-generation tools for high-throughput materials characterization. Qualifications: We are looking for a curious and collaborative student pursuing a Master's degree in Materials Science, Engineering, Computer Science, Applied Physics, Data Science, or a related field. The ideal candidate has an interest in machine learning, programming, and data-driven methods, and is motivated to apply these tools to challenges in materials characterization and development. You enjoy solving complex technical problems and working at the interface between materials science and artificial intelligence, with the opportunity to contribute to the development of next-generation analysis tools for advanced engineering materials. Project time The project is intended for a master thesis (30hp). The start date is January 2027 or can be mutually decided through negotiations. Further information This project is intended to be performed at Swerim in Kista Stockholm in close collaboration with Sandvik Cormorant in Västberga. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp). Contacts For further information please contact:  Johannes Brask, [email protected]  Application Apply by using the application function below. The application can be written in English or Swedish. Latest date for application is 15th of November. You will receive confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means we can fill the position before the deadline.

We are looking for a motivated Master’s student to join the AITHOS project, AI-based Trapping characterisation & Hydrogen Odelling in Steels. Hydrogen embrittlement is a major challenge for high-strength steels. One strategy to improve hydrogen resistance is to engineer carbide precipitates that trap hydrogen and reduce its mobility. The AITHOS project combines experiments, modelling and data-driven methods to understand this behaviour and support future development of hydrogen-tolerant steels. The Master’s thesis will be carried out primarily at Swerim and will contribute mainly to the experimental work in WP1 and WP2. Possible activities include:microstructural characterisation; hydrogen charging of steel specimens under controlled conditions; measurement and analysis of hydrogen trapping and release, including TDMS; mechanical testing of hydrogen-charged specimens; You will work at Swerim and collaborate closely with researchers from Uppsala University, SSAB and Ovako. The project therefore offers an opportunity to gain hands-on experience in hydrogen-materials research while developing a professional network spanning both academia and the Swedish steel industry. The project is suitable for a student who would like to develop expertise at the interface between advanced steel development, hydrogen embrittlement and experimental materials science. Your profile We are particularly interested in students with a background in:Materials Science and Engineering; Applied Physics or Engineering Physics; Mechanical Engineering or a closely related field. Additional Information The project is planned to be carried out during the spring semester of 2027 at Swerim in Stockholm. Upon successful completion of the Master's thesis (30 ECTS credits), Swerim will provide a compensation of SEK 50,000. Contact For further information about the project, please contact: Dr. Birhan Sefer, [email protected] Application  Please apply using the application link below no later than 30 November 2026. Please note that applications will be reviewed on an ongoing basis, and the thesis position may be filled before the application deadline if a suitable candidate is identified.

This master thesis is part of the Net Zero Impact Innovation-funded BAALSCRAP project (BAttery casings from ALuminum alloys with low carbon emissions produced from SCRAP), which aims to enable the production of battery cell housings from recycled aluminum scrap and thereby support the transition towards a circular and net-zero automotive industry. The project investigates how alloying elements and impurities originating from recycled feedstock affect manufacturing processes and material performance.  The thesis will focus on the relationship between microstructure and weld properties of recycled aluminum alloys for battery casing applications. The work will involve detailed characterization of welds using light microscopy and scanning electron microscopy (SEM), with emphasis on precipitates, intermetallic particles, segregation, porosity, cracking, and other welding-related defects. The influence of recycled alloying elements and residual impurities on weld quality and performance will be assessed and correlated with material properties. In addition, property calculations (e.g. crack susceptibility) will be used to predict weld performance as a function of alloy composition. The modeling results will be compared with the experimental observations to improve the understanding of microstructure evolution during welding and to help explain the relationship between alloy chemistry, weld microstructure, and weld performance. Desired Qualifications  We are looking for a highly motivated Master's student in Materials Science, Metallurgy, Materials Engineering, or a related field. The ideal candidate has an interest in experimental research and materials characterization, with a particular focus on microstructure-property relationships in metallic materials. Experience or coursework in microscopy, welding metallurgy, aluminum alloys, or electron microscopy is advantageous but not required. Additional Information The project is planned to be carried out during the spring semester of 2027 at Swerim in Stockholm. Upon successful completion of the Master's thesis (30 ECTS credits), Swerim will provide a compensation of SEK 50,000. Contact För ytterligare information om projektet, vänligen kontakta: Shirin Nouhi, [email protected]   Application  Please apply using the application link below no later than 15 November 2026. Please note that applications will be reviewed on an ongoing basis, and the thesis position may be filled before the application deadline if a suitable candidate is identified.

Project Description  Background  Laser Ultrasonics (LUS) is a powerful non-contact technique for in-situ characterization of metallic materials at elevated temperatures. In hot strip mill environments, LUS enables real-time monitoring of grain size evolution through analysis of ultrasonic wave attenuation and velocity, providing critical insight into recrystallization, grain growth, and phase transformations. This information is highly valuable for advanced process control strategies aimed at improving product quality, consistency, and resource efficiency.  The NAICE project (Non-destructive AI enhanced online grain size evaluation) addresses a key challenge in fossil free steel production: today’s steel mills lack real time information about the microstructure that governs material quality, leading to unnecessary energy use, scrap, and slow process adjustments. By upgrading the world’s only industrial laser ultrasonic grain size gauge and collecting a unique large dataset, the project will train models to enable AI enhanced measurement of the grain size and microstructure in real-time. This enables smarter process control, higher yield, and reduced emissions.  This thesis is the first of two planned master thesis works. The first will evaluate the best options using historical data sets, whereas the second will focus on using the full dataset and optimizing the model for industrial implementation.  If you want to know more about the LUS technology and the installation of the world’s only grain size gauge there is a recorded webinar here: https://www.youtube.com/watch?v=ZOpXUAPhEv0&t=665s   Thesis Scope  The thesis will focus on the application of machine learning and AI techniques to Laser Ultrasonic (LUS) data acquired in a hot strip mill environment. The available dataset consists of:  Raw LUS A-scan signals with multiple backwall echoes (20 µs duration, 1 ns resolution)  Grain size values calculated using an established conventional algorithm (serving as reference values)  The primary objective is to develop and evaluate AI-based approaches for:  - Grain size prediction directly from raw laser ultrasonic signals  - Microstructure state classification (e.g., recrystallized fraction, pancaking/aspect ratio)  A key part of the work will be to survey suitable AI architectures, such as time-series models, deep learning approaches, and alternative signal-processing-informed models, and assess their applicability to the LUS data.  Tasks  Survey and critically assess relevant AI/ML architectures for ultrasonic time-series data  Pre-process and structure high-resolution LUS A-scan datasets for model training  Develop and implement selected AI models  Perform benchmark comparisons between different approaches with respect to:   Prediction accuracy  Robustness to signal variability  Computational efficiency and suitability for real-time deployment  Analyze model interpretability in relation to known physical phenomena  Expected Outcomes  The thesis should deliver:  A structured comparison of candidate AI approaches for LUS-based grain size prediction  Prototype models demonstrating feasibility on the historical dataset  Recommendations for model selection in future industrial implementation  Desired Qualifications  We are looking for a motivated student with:  A background in computer engineering, engineering physics, materials science, applied mathematics, or similar  Strong interest in machine learning and signal processing  Experience with Python/Matlab and ML frameworks  Basic understanding of wave physics, ultrasonics, or materials science (meriting but not required)  Analytical mindset and ability to work with complex datasets  Additional Information  The work will be carried out in close collaboration with experts in Laser Ultrasonics and industrial process monitoring, providing a unique opportunity to contribute to cutting-edge development of AI-driven process control in steel manufacturing.  Project time  The project is intended for a master thesis (30hp), starting in spring 2027 or can be mutually decided through negotiations.  Further information  This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp).  Contacts  For further information please contact:   Mikael Malmström, [email protected]  Hampus Wikmark Kreuger, [email protected]  Application  Apply by using the application function below. The application can be written in Swedish (or English). You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means there is no explicit deadline.