The application of data science principles is accelerating in the manufacturing sector and the vision for this project is for ‘right first time’ intelligent processing. This is challenging in metal forming due to the complex interrelationship between the influencing factors. This project will address some of this challenge by developing a digital twin for sheet bending using numerical techniques with validation in collaboration with parallel experimental programmes.
This fully funded PhD studentship is an exciting opportunity
to radically change the sustainability and efficiency of manufacturing
processes. The successful candidate will work at WMG, University of Warwick to
apply Data science principles to manufacturing and forming, in close
partnership with Tata Steel UK (https://www.tatasteeleurope.com).
Sheet bending is a common metalforming process in the transport sector and is intrinsically linked to process set-up, sheet surface quality and the bulk properties of the steel. However, it is highly susceptible to ‘springback’, which detrimentally affects the final dimensions of the component and to cracking failure. This research will bring about new levels of efficiency and sustainability by investigating the digitisation of the bending process. This ICase PhD will run alongside an existing Tata Steel PhD project aimed at developing a physical, instrumented bending capability at WMG, Warwick.
The research will consider, but is not constrained, by:
1. The design of experimental programmes to correlate and model sensor data to system performance.
2. The development of novel ‘intelligent’ digital systems that use sensor data to enable real-time models / digital twins of the process that predict process performance.
3. The exploration of inverse analysis to tune/train the model to the bending test for a range of bending set-up and steel behaviour.
4. The development of the digital system into a prototype process for validation.
Essential and desirable criteria
The ideal candidate will have a strong academic background in one or more of the following areas: experimental design, data processing, mathematical modelling and manufacturing engineering.
Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) in Materials Sciences (including Metallurgy), Mechanical Engineering, Data Science or related disciplines. A good command of English is essential for the position.