Machine Learning of Automotive materials behavior
Machine Learning of Automotive Materials Behaviour
Research Group Activity
| Materials Informatics |
Project Description
| Development of light-weighting automotive body structures is crucial nowadays in automotive industry to minimise fuel consumption and emissions. Among engineering materials, steel is widely used in automobiles because of its abundance, cost effectiveness and ease in manufacturing capability as compared to other materials. For Body In White (BIW) applications, cold-rolled annealed multiphase steels (tensile strength, TS > 600 MPa) such as DP, CP, transformation-induced-plasticity (TRIP), bainitic/martensitic and ultra-high-strength-steel (UHSS) and cold-rolled annealed high-strength-low-alloy (HSLA) steel grades (TS ~ 600 MPa) are extensively used. Microalloying content of Nb, Ti, V or Mo in these HSLA grades is limited to avoid inhibition of recrystallization and maintain precipitation strengthening. Static recrystallization and precipitation behaviour of cold-rolled low-carbon microalloyed steel during sub-critical annealing is relatively less understood. Initial grain size, cold-rolled deformation and chemistry of steel play significant role in recrystallization kinetics. Researchers have carried out experiments and modeling to predict recrystallization and precipitation behaviour in steel during annealing. Machine learning is relatively newer concept to study annealing process of steels. Artificial Neural Network (ANN) provides an opportunity to explore the recrystallization behaviour of steels. In this project, 8 weeks of internship, student will perform literature review on the application of ANN to study recrystallization behaviour of steels. Student will utilise current and previous published and available data on grain size, cold-rolled deformation, annealing condition etc. as inputs to predict recrystallization fraction as outputs during annealing process of steel. |
Student Level
Open to both undergraduate and postgraduate students
Location
This project can be completed remotely.
Skills you can learn from this project
| Academic writing skills Presentation to audience skills Data collection and analysis skills Mathematical and Programming skills Networking skills with academic and industry partners |
Required Skills
| Background in Machine learning is required Programming skills are required Student who likes reading and digging deep into literature and finding new knowledge. |
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