Rolling is a metal forming process to produce a strip with particular dimensions and mechanical properties. While its thickness decreases, the strip is elongated but also experiences expansion in width, referred to as lateral flow. The ambition to make steel production more sustainable, combined with increased customer demands regarding product specifications, requires the manufacturing of dimensionally accurate products while reducing waste during production as much as possible. This is achievable only with exacting process control mediated by accurate and efficient mathematical models describing the material deformation during the process, considering key influencing factors such as process temperature, differences between the steel grades, and interaction of rolls and the strip.

This project strives to develop a new hybrid modelling strategy blending physics-based and data-driven approaches. The physics-based semi-analytical prediction will provide a fast solution to the rolling problem using model order reduction, while the data-driven machine learning correction will convert the solution to a high accuracy. The resulting fast, accurate, and comprehensive lateral flow model for rolling process will enable improved process control for various mill configurations.

In this project, you will report your research during bi-weekly meetings of our research group and frequent meetings with the industrial partner. You are encouraged to present your results at international scientific conferences, and publish them in academic journals. Furthermore, as a researcher, you will be encouraged to tutor MSc students who do their final assignment on parts of the current research project. Your doctoral advisors will be Dr. habil Celal Soyarslan and Prof. Dr. Ton van den Boogaard.

• an MSc degree in Computational Mechanics, Mechanical Engineering, Applied Physics, or a related field with excellent grades
• particular interest in modelling of production processes
• a background in nonlinear solid mechanics, computational methods, materials science and/or data science
• strong programming skills
• a high degree of responsibility and independence
• strong communication skills for effective academic and industrial collaboration
• proficiency in English, both verbal and written (IELTS minimum score 6.5 or TOEFL-iBT minimum score 90)

• a dynamic and international environment, combining the benefits of academic research with a topic of high industrial relevance
• excellent working conditions in an exciting scientific environment and a green and lively campus
• a fulltime four year PhD position
• excellent mentorship and facilities
• a professional and personal development program within Graduate School Twente
• a starting salary of € 2.541 in the first year and a salary of € 3.247 in the fourth year gross per month
• a holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%
• minimum of 29 holidays per year in case of full-time employment
• full status as an employee at the UT, including pension and health care benefits