Final thesis "Development of machine learning algorithms for process optimization"

Job ID:6306

The Hirschvogel Group is a company on the move. As a technology-open innovation partner and component manufacturer in the automotive industry, we are shaping the transformation of the mobility sector. We are present in the world's key market regions. Our forged and further processed steel and aluminum components are installed by all major vehicle manufacturers and suppliers. We offer components that are drive-independent as well as geared towards e-mobility and hydrogen-based drive concepts.

From forged parts to finished, ready-to-install products – this is the path Hirschvogel Komponenten GmbH in Schongau follows – with precision and perfection. The pillars of success: around 1,000 employees who give the complex formed raw parts at Hirschvogel the necessary finishing touches. With state-of-the-art finishing lines and an impressive level of automation and digitalization.

This thesis aims to develop and implement machine learning algorithms for optimizing machining processes at Hirschvogel. The work covers the following aspects:

Your tasks:

• You analyze existing machining data and identify relevant parameters for process optimization.
• You will develop suitable machine learning models to predict surface quality, dimensional accuracy and other quality characteristics.
• They implement the developed algorithms in a test environment for machining processes.
• Using real data from machining, you evaluate the models and optimize the approaches.
• You will develop a concept for integrating the developed solution into the existing machining environment at Hirschvogel.

Depending on your interests, the following focus is possible:

• Tool management: prediction of tool wear and optimal planning of tool changes in CNC machines

• Surface quality prediction: Early detection of surface defects through analysis of machining parameters

• Energy efficiency: Optimizing the energy consumption of machining centers while maintaining component quality

• Process stability: Development of models to improve process stability when machining complex geometries

• Chip break monitoring: Intelligent monitoring and control of chip breakage for optimal machining conditions

Your profile:

• Subject:Computer Science, Mechanical Engineering, Industrial Engineering, Data Science or a comparable course of study
• Interest:Interest in machining technology and manufacturing processes
• Expertise:Basic knowledge of machine learning and data science
• IT skills:Initial programming experience in Python, R, or comparable languages; ideally, initial experience with common ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• Personality:High degree of initiative and willingness to contribute your know-how to the team; independent and structured way of working

Our benefits:

• Financial benefits:travel allowance, additional accident insurance
• Health:Health center with company doctors and integrated fitness studio, health courses, subsidized company restaurant, external employee counseling
• Further training:Language courses, intercultural training and a wide range of training options to strengthen your professional and methodological skills
• Workplace:Own notebook, free use of Microsoft Office from home, flexible working hours adapted to your studies
• Events:Employee parties, Hirschvogel Ski Cup, football and curling tournament

Here’s what you can look forward to:

• Intensive supportfromexperienced expertsin the fields of machining and digitalization at Hirschvogel

• Access tostate-of-the-art CNC technologyand extensive data sets from real machining processes

• Opportunity toimplement and test innovative conceptsin a leading international company

If you have any questions, please contact Elissa Nitsch at +49 8243 291 1741.

We look forward to receiving your online application.

We look forward to receiving all applications!

Inclusion and equal opportunities are important to us, which is why gender, age, sexual identity, ethnic origin, religion or belief, possible disability or similar characteristics play no role for us.