Prof Rachel Thomson

Head of Department / Director of the Materials Res

Loughborough University, Department of Materials

Loughborough University
Loughborough
Leicestershire
LE11 3TU
United Kingdom
PH: 44 (1509) 223155
Email: [email protected]

Background

Rachel read for a degree in Natural Sciences at Newnham College, Cambridge University between 1986 and 1989, specialising in Physics. She then carried out her PhD in the Department of Materials Science and Metallurgy, also at Cambridge, between 1989 and 1992. For the following 3 years she held the position of Research Fellow in the same department, supported by SERC (now EPSRC) and Rolls-Royce plc. Between 1993 and 1995 she was awarded a non-stipendiary Research Fellowship at Darwin College, Cambridge University. In 1995, Rachel moved to a lectureship in the Materials department at Loughborough University, being promoted to Senior Lecturer in 1999 and to a Personal Chair in 2002. She became the Director of the Materials Research School, one of 5 multidisciplinary Research Schools in the University, in September 2006. Became Head of Department in 2011.

Research Interests and Activities

The overall aim of my research is to develop models to enable the prediction of microstructure and mechanical property evolution in metal alloys as a function of the initial processing and also subsequent service. This is leading to both the development of new alloys and a greater understanding of existing alloys, allowing life extension methodologies to be implemented. The ideas have been applied to a range of industrial alloys; steels, Ni-based superalloys, cast irons and Al-Si casting alloys. Achievements to date include:

  • Extensive studies of carbide and nitride evolution in power plant steels, including the first measurement of composition profiles through carbides in chromium steels at the atomic scale
  • A comprehensive understanding of microstructural evolution in Ni-based superalloy systems, which has led to a successful patent application for a method of life prediction
  • Prediction of weld bead shape, chemistry and mechanical properties using neural network techniques
  • Development of a completely integrated model which can predict the microstructure and simple mechanical properties for austempered ductile irons
  • Contribution to the industrial development of new multicomponent Al-Si casting alloys

In the different alloy systems the same strategy has been utilised, however, it is also necessary to develop a complete understanding of the relationship between microstructure, properties and processing routes which involves a range of modelling and characterisation techniques as appropriate. My research involves the prediction of equilibrium phases, development of kinetic models for evolution of microstructure as a function of time, models relating macroscopic properties to microstructure and experimental characterisation using a variety of advanced microscopy techniques, from the atomic scale to bulk property determination.

Major Qualifications

MA, PhD Cambridge

Major Publications

  • ‘Prediction of Multiwire Submerged Arc Weld Bead Shape using Neural Network Modelling’, Thomson RC, Ridings GE, Thewlis G, Science and Technology of Welding and Joining, 7[5], 265-279, (2002).
  • ‘Microstructural & Mechanical Property Modelling of Austempered Ductile Iron’, Thomson RC, Putman DC, International Journal of Cast Metals Research, 16[1-3], 191-196, (2003).
  • ‘A Phase-Field Model for the Solidification of Multicomponent and Multiphase Alloys’, Thomson RC, Qin RS, Wallach ER, Journal of Crystal Growth, 279[1-2], 163-169, (2005).
  • ‘Characterization of Isothermally Aged Grade 91 (9Cr-1Mo-Nb-V) Steel by Electron Backscatter Diffraction’, Thomson RC, Sanchez-Hanton JJ, 460-461, 261-267, (2007).

Ask A Question

Do you have a question you'd like to ask this Expert?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.