PhD position Prediction of local material response to welding – Technical University of Delft, Delft (the Netherlands)

////PhD position Prediction of local material response to welding – Technical University of Delft, Delft (the Netherlands)

Introduction

The design requirements of new steel grades for the automotive industry are driven by the targeted application. Optimised strength-formability properties are often the first design objective. Nevertheless, other properties, such as fatigue strength, edge ductility or weldability have gained increased attention the last years, since improved strength-ductility performance often comes at the cost of decreased fatigue, edge ductility or weld behaviour. Currently, weld testing can be done only in the later stages of material development, when larger quantities of a trial material are available. In order to capture potential problems as well as solutions for acceptable weld behaviour in the earlier stages of material development, models should be established that can predict weld microstructure evolution during the most common automotive joining processes.

Organization

The Department of Materials Science and Engineering (MSE) of the Delft University of Technology, undertakes coherent and innovative research dedicated to developing, producing, characterising and manipulating materials, with a focus on metals. The aim is to develop fundamental understanding forming the basis for better performance, a longer life cycle with preservation of functionality, while at the same time optimising sustainable use of resources and minimising environmental footprint.

The Joining and Additive Manufacturing section of the MSE department has a strong track record in welding technology and metallurgy and is currently developing the research field in additive manufacturing. The research of the group follows both experimental and numerical approaches to obtain a better physical based understanding of the phenomena encountered. For more information: https://www.tudelft.nl/en/3me/departments/materials-science-and-engineering/research/joining-and-additive-manufacturing/

Function

This PhD project forms part of the Digitally Enhanced New Steel Product Development (DENS) program, in which Tata Steel Europe, Materials innovation institute (M2i) and several academic partners collaborate to enable the development of new generations of advanced materials for e.g. the automotive industry.

Digitally Enhanced New Steel Product Development (DENS) program

Significant progress has been made in the past decades in the development of advanced models that describe the behavior of steel during processing and subsequent applications. However, the quantitative application of through process models in new steel product development still lacks predictivity. In modern steel grades, parameters of the steel production process have a significant influence on the final material properties. Furthermore, the trend towards complex multi-phase microstructures requires very sophisticated models to describe their mechanical properties in a predictive manner. The main scientific challenge addressed in this program is to integrate state-of-the-art models in a single through process model framework that can be applied in practice for new steel product development.

Currently, weld testing is done when larger quantities of a trial material is available. To capture potential problems and solutions for acceptable weld behaviour in the earlier stages of material development, we aim to develop a model that can predict weld microstructure evolution during the most common automotive joining processes. We will assess whether the through-process models, developed for the metal production route, can be adapted to describing the influence of welding on local structure and mechanical properties. To significantly increase computational speed, we will examine the application of a cellular automata (CA) model to the non-equilibrium conditions during welding. To assess the strain and stress distributions, we will also focus on the application of plasticity models.

Requirements

We are searching for enthusiastic candidates holding a MSc degree in physics, chemistry, materials science or similar field. The candidate must have knowledge on physics of materials, solid state physics, solid-solid phase transformations and mechanical behaviour. Moreover, the candidate should have:

  • affinity for computational calculation as well as a solid background on mathematics and numerical methods.
  • an “academic attitude” with excellent analytical skills, taking initiative, inventiveness, curiosity and critical thinking about the research approach and results.
  • strong communication skills (high English proficiency), ability to integrate, and have good social behaviour.

Offer

The TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

The PhD. candidate will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.phd.tudelft.nl for more information.

Information

Minds for Innovation is the recruitment partner for this project. For more information regarding recruitment please contact Jolanda de Roo. E: j.deroo@mindsforinnovation.nl, T: + 316 572 913 50

For more information on the project, please contact Prof. Ian Richardson. E: I.M.Richardson@tudelft.nl

Application

Application documents (in PDF format) must contain:

  • letter of motivation containing why you are interested in this specific project and why you would be a good candidate (refer to vacancy number 3ME17-74 in the subject)
  • detailed curriculum vitae and contact information of two potential referees
  • transcripts of BSc and MSc grades
  • transcript of English test

Apply before the deadline of July 31st 2018.

As one of the first selection steps, you will be invited to participate on our specialised online assessment. If you would like to know more about why we use this and how we have set it up, then check out our website.

2018-06-20T02:10:08+00:00 June 6th, 2018|