Master's Thesis: AI-Based Vision for Automated Quality InspectionBackground

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.

What You Will Do

Final inspection of blocks in our machining line is currently performed manually.

This method is resource-intensive and involves a risk that blocks with quality deviations pass through the inspection process.We therefore want to investigate the possibilities of using AI-based vision to automate or support the quality inspection and improve its reliability.

Assignment

The purpose of this thesis is to investigate how modern vision systems and AI-based methods can be used to detect quality deviations occurring in the machining line.

Examples Of Deviations Include

PorosityScratchesImpact marksUnmachined holesOther visually detectable quality deviations

The work will include mapping suitable cameras, sensors, lighting solutions, vision systems and AI methods.

You will also assess how effectively different solutions can detect each type of deviation.

Expected results

The Final Report Should Include

Proposals for suitable vision systems and technical solutionsAn assessment of detection capability and reliability for different types of deviationsIdentification of limitations, risks and potential training data requirementsAn overview of integration possibilities with the existing machining lineA recommendation on whether the work should be continued, and if so, which areas should be prioritised

The goal is to evaluate whether AI-based vision can provide a sufficiently high and consistent detection rate to be a viable alternative or complement to the current manual final inspection. The focus should therefore be on quantifying the performance of the solution rather than assuming that 100% of all deviations can be detected.

Who are you?

We Are Looking For a Student Who

Is studying Engineering Physics, Electrical Engineering, Computer Science, Mechatronics, Mechanical Engineering or a related fieldHas an interest in computer vision, machine learning and industrial automationIs analytical and enjoys combining technical literature studies with practical testingHas experience with image processing or machine learning, which is an advantageIs able to work independently and present technical results clearly

Application

We warmly welcome your application no later than October 30, 2026. We look forward to learning more about you and your interest in helping shape the future of manufacturing at the Volvo Group

Contact

For questions regarding the thesis project, please contact: johan.gustavsson.7@volvo.com

We value your data privacy and therefore do not accept applications via mail.

Who We Are And What We Believe In

We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.

Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.

Trucks Technology & Industrial Division hire team players who are ready to create real customer impact. Our decentralized teams work close to our customers, with speed and autonomy, to build what they truly need.

Join us to collaborate on innovative, sustainable technologies that redefine how we design, build, and deliver value. Bring your curiosity, your expertise, and your collaborative energy, and together, we’ll turn bold ideas into tangible solutions for our customers and contribute to a more sustainable tomorrow.