Alexander Korsunsky

Professor
Editor-in-Chief of Materials & Design
University of Oxford, UK

Biography: Alexander Korsunsky is Professor and Fellow Emeritus at Trinity College, Oxford, where he previously served as Dean and Vice-President. He is world-leader in engineering microscopy of materials for the optimisation of design, durability and performance. He published prolifically on eigenstrain theory, covering the elaboration and application of the methods for solving the forward and inverse problems of eigenstrain. He spearheaded the development of experimental techniques for residual stress evaluation, principally in the field of synchrotron X-ray scattering, imaging, spectroscopy and rich tomography, and in the field of electron-ion microscopy for material removal and strain relief methods. He serves as the Editor-in-Chief of Materials & Design.

Invited Lecture: The Rational Experimental-Computational Correlation (RECC) – reliability improvement toolkit for materials technologies in aerospace design

Abstract: The principal objective of modern aerospace technologies is to develop innovative designs, deliver serial production and ensure efficient safe exploitation of civil aircraft and transport systems. This combination of objectives is somewhat contradictory, in that new designs traditionally require an extended period of testing and validation, effectively negating the advantages of rapid prototyping and manufacturing by modern techniques, such as additive production of metals (APM). The principal point here is that APM for aerospace places emphasis not merely on the obtention of the required shape, but rather of guaranteed and certified service properties: static and dynamic strength, fatigue durability, fracture toughness etc. To address this outstanding issue, the author proposed and advanced the approach known as the Rational Experimental-Computational Correlation. The core of this approach consists of the systematic combined use of empirical and modelling evidence across the scales to ensure the validity and reliability of design. The practical application of this approach to any modern production process, such as APM, allows navigating the chain of materials science interrelationships: composition – fabrication – structure – properties, whilst verifying the validity of each link through both testing and advanced numerical simulation, and expressing the results in the form of machine learning-based predictive software tools. Illustrations of this approach will be provided.