Project Overview
RISE is collaborating with industrial partners to advance precision manufacturing using Laser Powder Bed Fusion (LPBF) for demanding applications (e.g. energy and nuclear-related components). A key enabler is the use of advanced build-preparation and process-development software that allows detailed control of laser paths, scan strategies, and locally tailored process parameters.

The goal of this thesis is to evaluate such software in practice, from build preparation through printing of representative test geometries and industrially relevant use cases, and to demonstrate the benefits of advanced laser and scan strategy control for dimensional accuracy, surface quality, and build efficiency.

Thesis Aim and Main Tasks
The thesis focuses on software-driven process development and experimental validation in LPBF. Main tasks include:

• Preparing builds with an advanced LPBF process-development / build-preparation tool

• Designing and selecting test geometries and simplified use cases that highlight critical features (overhangs, thin walls, channels, etc.)

• Setting up and comparing different scan strategies and parameter configurations (e.g. contour/infill strategies, locally adapted parameters)

• Printing selected builds on LPBF equipment and documenting the process

• Evaluating outcomes in terms of, for example, geometric deviations, surface quality, support usage, build time and robustness

• Summarising the findings in a structured way, with recommendations on how such software and laser control can support precision and first-time-right manufacturing

A brief literature review on LPBF process development, build-preparation workflows, and laser/scan strategy control will form the scientific foundation of the work.

Qualifications
We are looking for one Master’s student (30 credits) with the following profile:

Educational background

• Enrolled in a Master’s programme in Materials Engineering, Mechanical Engineering, or a closely related discipline

Skills and interests

• Strong interest in lab work, experimental methods, and additive manufacturing

• Interest in process development, build preparation, and the connection between process parameters and part quality

• Enjoys structured data analysis, result comparison, and clear documentation

Meriting knowledge and experience

• Knowledge of Additive Manufacturing, preferably metal AM / LPBF

• Familiarity with AM-related software (build-preparation, CAD/CAE, or simulation tools)

• Experience with Python programming (for data handling, simple scripting, or automation) is a strong advantageI

• Interest in Design for Additive Manufacturing (DfAM) is a plus

Practical Information & Application

Extent: 30 Credits
Start: Beginning of February 2026
Compensation: 10,000 SEK for travel, materials and the like after the project is completed and approved

Apply with your CV, transcript of records and answer the motivation question in your application.

Contact person: Saeed Khademzadeh (saeed.khademzadeh@ri.se)