Doctoral Thesis: AI-assisted Solutions in Sustainable Aluminium Forming Products for Crashworthiness and Impact Applications

The main tasks will be to conduct advanced mechanical characterisation of sustainable aluminium alloys and the development of AI-assisted models.

The student is expected to:

• Acquire the necessary skill to use of conventional testing devices (universal testing machine), as well as other more unorthodox experimental setups such as Hopkinson bar systems and a biaxial hydraulic testing machine.
• Develop the theoretical and mathematical background to program computational constitutive models.
• Confidently employ the latest emerging AI technology to assist the mechanical problems in forming and constitutive modelling.
• Attend national and international conferences.
• Publish peer-review articles in high impact indexed journals.

Start of thesis: within a maximum period of one week after the candidate has been admitted by the Academic Committee of the Doctoral Programme.

Type of contract:

• Researcher in Training contract associated to a predoctoral grant (see grant conditions here)
  • Duration: 4 years
  • Amount of aid: the amounts of aid for each year are indicated below.

Mondragon Goi Eskola Politeknikoa // Escuela Politécnica Superior

Mondragon Unibertsitatea es una Universidad práctica, innovadora y comprometida, centrada en el desarrollo de las personas, orientada a las necesidades de la empresa y la sociedad, pensada para hacer frente a los desafíos del mundo real y donde el conocimiento y su aplicación no tienen fronteras.

The transportation sector is one the largest contributors to European Union (EU) greenhouse gas (GHG) emissions and as such, is strongly impacted by the EU regulations that aim to reduce these by at least 55% by 2030, compared to 1990 levels. This has drastically changed the transportation landscape, especially affecting the car and van fleets, switching from fossil fuel powered Internal Combustion Engine (ICE) vehicles to those that are either plug-in hybrids (PHEV) or battery electric (BEV). Unfortunately, the electrification of the transportation is not enough to accomplish the ambitious goal of being a climate-neutral continent by 2050, as more actors exist in the automotive value chain. Material suppliers that offer for example, cast, rolled or extruded solutions and Original Equipment Manufacturers (OEM) that utilising forming processes create end products, heavily influence the GHG emissions of such value chain. From the point view of the mechanical and structural design, the electrical mobility has brought two of the most severe changes with respect to the ICE vehicles are (i) the overall weight increase of the vehicles due to the weight of the battery pack and (ii) the drastic motor configuration change from the front or rear to the bottom of the cars. These changes imply that the OEMs together with the car manufacturers have to certify new products and components that might have a severe repercussion on the safety performance of the vehicle. The weight increase together with the battery life restrictions has pushed the industry towards a lightweight mindset increasing the aluminium alloy usage in 25-27% with respect to a typical ICE.

The main objective of the project in which this PhD will be encompassed, is to develop intelligent and technologically innovative solutions that aim to achieve metal forming industry's ambition of carbon footprint and cost reduction, oriented towards the most demanding applications where extreme loading conditions, i.e. high loading rates and elevated temperatures, apply. To achieve this, it will investigate the use of scrap tolerant or low carbon aluminium alloy products, either stamped or extruded, for EV specific and armour components in crashworthiness and impact scenarios aiming to reduce the GHG emissions. To achieve this target, and to tackle the afore-mentioned challenges we will:

• Assess the feasibility of use and impact of low carbon and/or scrap-tolerant alloys
• Develop experimentally-validated high-fidelity advanced computational constitutive models
• Develop the technology to design more efficient hot forming processes
• Mechanics-informed data-driven machine learning models for faster and more efficient analysis of forming processes and safety assessment
• Investigate the performance of aluminium protective structures designed for EVs

This four-year predoctoral position is fully funded by the "Agencia Estatal de Investigación" (AEI) through the iSAFE (PID2024-162078OB-I00) "Generación de Conocimiento 2024" project. The funding includes the tuition fees as well as a dedicated budget for short stays (3 month) abroad.

The project will be carried out in the Advanced Materials Forming Group at the Faculty of Engineering of Mondragon Unibertsitatea, with campuses in Mondragon, Bilbao (Zorrozaurre) and Hernani (Galarreta) under the supervision of Dr. Borja Erice, in collaboration with Dr. Miguel Costas from the Norwegian University of Science and Technology (NTNU).

Aid

YEARS 1 y 2 23.476,48€ gross

YEAR 3 25.153,37€ gross

YEAR 4 31.441,72€ gross

The beneficiaries of this aid are exempt from paying the doctoral program tuition fees and thesis reading fees.

* This grant must be renewed annually after verification of the doctoral student's adequate performance.

Do you want to know us better? Visualise the video

We are looking for proactive people who are agents of change; people who are committed and involved in reality, who seek to transform it, giving the best of themselves.

• Educational requirements:
  • The candidate must have Master's degree (or about to be completed) or equivalent in Mechanical, Civil or Aerospace Engineering, Materials Science and Engineering or any other related degree.
• Specific knowledge:
  • Strong background in solid mechanics, specifically mechanics of materials.
  • Capacity for teamwork in an interdisciplinary and international environment.
  • Self-motivation and willingness to perform research.
  • Creativity in problem solving.
  • Ability and eagerness to learn new skills outside own discipline.
• Languages:
  • High level of written and spoken English (C1 or equivalent)
• The following is a plus:
  • Programming skills (python, FORTRAN, Matlab), machine learning and finite element-based physics simulation knowledge is highly desirable.