Thesis work 30 hp-Data-Driven Prediction of Product Quality and Tool Life from Machine Tool Signals

Introduction
At Scania Transmission Manufacturing – do you want to be part of shaping the future of manufacturing processes by developing smart solutions to improve production efficiency and support our operators?

Within Transmission Manufacturing at Scania, we are entering an expansive development phase. Our machining equipment plays a central role in production. Here, we see great potential in leveraging data signals and digital tools to improve machine availability, optimize machining processes, and at the same time facilitate the work of our operators.

Thesis project at Scania is an excellent way of making contacts for your future working life. Many of our current employees started their career with a thesis project.

Background
Modern production generates massive high-frequency machine signals, making factories a rich ground for data science. By combining the insight of experienced engineers with data-driven methods, we can transform raw machine data into actionable insights for product quality and tool life.

Objective

This thesis aims to develop data-driven methods for predicting product quality and tool life from machine tool signals, integrating domain expertise with machine learning to enable smarter, more precise, and efficient manufacturing decisions.

Job description

You will investigate how machine tool signals can be used to predict product quality and tool life in transmission manufacturing. This includes reviewing state-of-the-art research, collecting and preprocessing production data, and identifying features that connect signals to engineering outcomes. You will develop and validate predictive models, integrating domain expertise with data-driven methods, and evaluate their industrial applicability. Finally, you will propose strategies for implementation in real production environments.

The output of this thesis would be:

• A structured dataset and feature set linking machine signals to quality and tool life.
• Validated predictive models with benchmarking against industrial performance metrics.
• Recommendations for integrating predictive insights into production decision-making.
• A roadmap for industrial implementation and future development.

Education/program/focus

Master students in industrial engineering, production engineering

Number of students: 1

Start date for the thesis work: Jan 2026

Estimated time required: 20 weeks

Supervisors

Poules Al Atallah

+46721439538

Contact person
Mohammad Haddadzade

+46700814150   

Application:
Your application must include a CV, personal letter and transcript of grades

A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.

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