Siemens Careers

Master Thesis Student spring 2019 "Feature Engineering for fault detection in AM using Machine Learning and Statistics"

Finspang, Sweden
Quality Management

English (UK)

Job Description

Are you a master student planning to write your Master Thesis during spring 2019?

Join us on our journey into the future #FutureMakers#Siemens

Be part of an open and dynamic workplace where professional and personal development is high on the agenda. By making sustainable energy solutions more cost effective, developing new technologies for the future's smart industry and electrifying passenger and freight transport, we make reality of our vision of a sustainable world.

We are now looking for a student to take on the assignment” Feature Engineering for fault detection in AM using Machine Learning and Statistics

This MSc thesis is part of Siemens efforts to integrate data-driven decisions in all the company structure. The final goal of this assignment is to develop a methodology that can use sensor data to explain manufacturing defects in our 3D printing process.



 Project description



  • Understanding the signals that are important in the additive manufacturing process and perform quality signal assessment to study if our measurement system is reliable enough.
  • State of the art review of data analytics techniques applied to the AM field
  • Explicit definition of the problem and methods selected to perform pattern recognition and correlations analysis.
  • Study of the possibility of incorporate dimensionality reduction techniques to the data as PCA, LDA or other relevant algorithms.
  • Finding patterns and correlations that can explain the manufacturing defects.
  • Improvements needed to the data acquisition system in case that any underlying pattern has been found.
  • Outlook and future perspective.


Students will be working closely with domain experts in additive manufacturing and material science. Additionally, they will have the co-support of the data analytics team from Digital Intelligence department and they will be collaborating closely with our data scientists.



 Your Profile: 



  • The project is suitable for students with academic background in machine learning, computer science, mechanical engineering, aerospace engineering, mathematics or another relevant field.
  • As a student you are self-sustaining and able to find your own ways forwards.
  • Besides, you have strong analytical and signal processing skills.
  • You hold good programming skills (preferable: Python, R or Matlab)
  • We consider meritorious skills the knowledge of machine learning oriented libraries (scikit-learn or caret) and data handling libraries (Pandas or tidyverse). 

Your application should include:


  • CV
  • Cover letter
  • If you have it, previous work/essays related with the topic.


Do not hesitate - apply today via ref nr 91171 and no later than 19 December. For questions about Additive Manufacturing contact Andreas Graichen on +46 122 82230 or for questions regarding the project contact Erik Ärlebäck on +46 122 81291.

Type of contract: 
Fixed term – approximately 5 months. 
Compensation type: hourly paid. 

Trade Union representatives:
Christine Lindström, Unionen, 0122-817 28
Simon Bruneflod, Sveriges Ingenjörer, 0122-842 24
Jan Lundgren, Ledarna, 0122-812 33
Kenth Gustavsson, IF Metall, 0122-815 25


In this recruitment we renounce all calls relating to advertising and recruitment support.

Job ID: 91171

Organisation: Power Generation Services

Experience Level: Student (Not Yet Graduated)

Job Type: Full-time

Can't find what you are looking for?

Let's stay connected