PhD Position: Improved Temper Roll-Force Prediction Through Microstructural Modelling

Steel is extensively used in sheet form across a wide range of applications. Two critical processes that significantly influence the usability of steel sheets are annealing and temper rolling. Annealing resets the material's microstructure, enhancing strength and formability, but it can also lead to the undesirable yield point phenomenon. This phenomenon causes localized deformation known as Lüders bands, which adversely affect the material's mechanical properties and surface quality.

Temper rolling is employed to eliminate the yield point phenomenon, but accurate prediction of the roll force during this process is challenging due to a lack of understanding of the underlying microstructural mechanisms. If the roll force is not precisely controlled, it can negatively impact the final properties of the steel sheet, undermining previous optimization efforts and increasing scrap rates.

This project aims to develop a new material model based on an improved understanding of the yield point phenomenon through innovative testing and microstructural modelling. By focusing on the mobility and density of dislocations that contribute to plastic deformation, this physics-based approach will enhance predictive capabilities. The model will be integrated into temper rolling simulations to accurately predict roll force, surface properties, and microstructural outcomes. Additionally, it will facilitate fast, online prediction of roll force, directly impacting scrap reduction and process efficiency.

Your Role

• Participate in cutting-edge research under the guidance of leading experts in the field.
• Develop a novel constitutive material model that incorporates the yield point phenomenon at the macroscale.
• Design and conduct mechanical characterization experiments (e.g., uniaxial tensile tests, shear tests) using advanced techniques like Digital Image Correlation (DIC).
• Implement the material model in finite element simulations (e.g., using Abaqus) to simulate the temper rolling process.
• Enhance existing analytical tools for fast and accurate roll-force prediction.
• Collaborate with an interdisciplinary team, including members from the University of Twente and industrial partner Tata Steel Europe.
• Report your research findings in bi-weekly meetings and at international conferences.
• Prepare and publish your research in high-impact academic journals.
• Complete a doctoral thesis within the project duration.

Please submit your application before June 15, 2025, using the "Apply now" button, and include the following:

• Curriculum vitae
• Letter of motivation
• Transcripts from your BSc and MSc courses
• IELTS or TOEFL score
• Contact information of 2 references

For more information, you can contact Dr. habil Celal Soyarslan via phone: +31 5 3489 7499 or email: c.soyarslan@utwente.nl