Siegkas, P., 2022. Generating 3D porous structures using machine learning and additive manufacturing. Materials & Design, 220, p.110858.

Okenyi, V., Bodaghi, M., Mansfield, N., Afazov, S. and Siegkas, P., 2022. A review of challenges and framework development for corrosion fatigue life assessment of monopile-supported horizontal-axis offshore wind turbines. Ships and Offshore Structures, pp.1-15.

Apps, C., Dawson, L., Shering, B. and Siegkas, P., 2022. Grip socks improve slalom course performance and reduce in-shoe foot displacement of the forefoot in male and female sports players. Journal of sports sciences, 40(12), pp.1351-1359.

Siegkas, Petros. "A Computational Geometry Generation Method for Creating 3D Printed Composites and Porous Structures." Materials 14.10 (2021): 2507.

Iizuka, Makoto, et al. "Large deformation finite element analyses for 3D X-ray CT scanned microscopic structures of polyurethane foams." Materials 14.4 (2021): 949.

Donat, Cornelius K., et al. "From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury." Brain 144.1 (2021): 70-91.

Melia, Georgia, et al. "Insoles of uniform softer material reduced plantar pressure compared to dual-material insoles during regular and loaded gait." Applied Ergonomics 91 (2021): 103298.

Elmrabet, Nabila, and Petros Siegkas. "Dimensional considerations on the mechanical properties of 3D printed polymer parts." Polymer Testing (2020): 106656.

Siegkas, Petros, David J. Sharp, and Mazdak Ghajari. "The traumatic brain injury mitigation effects of a new viscoelastic add-on liner." Scientific reports 9.1 (2019): 1-10.

Pedrazzini, S., Galano, M., Audebert, F., Siegkas, P., Gerlach, R., Tagarielli, V. L., & Smith, G. D. W. (2019). High strain rate behaviour of nano-quasicrystalline Al93Fe3Cr2Ti2 alloy and composites. Materials Science and Engineering: A, 764, 138201.

Siegkas P, Petrinic N and Tagarielli VL. Measurements and micro-mechanical modelling of the response of sintered titanium foams, Journal of the Mechanical Behavior of Biomedical Materials, 2016, 57: 365-375

Simone F, Siegkas P, Barbieri E and  Petrinic N. A new method for the generation of arbitrarily shaped 3D random polycrystalline domains J. Computational Mechanics, 2014

Siegkas P, Tagarielli VL, Petrinic N and Lefebvre LP. Rate Dependence of the Compressive Response of Ti Foams. Metals (special issue), 2012, 2: 229-237.

Siegkas P, Tagarielli VL, Petrinic N and Lefebvre LP. The compressive response of a titanium foam at low and high strain rates, 2011, J Mater Sci, 46: 2741–2747

Conference

Montanari, Mattia, et al. "Nurbs-based isogeometric analysis for ballistic evaluation of titanium plates." (2016).

Siegkas, P., V. Tagarielli, and N. Petrinic. "Modelling stochastic foam geometries for FE simulations using 3D Voronoi cells." Procedia Materials Science 4 (2014): 221-226.

Engineering Education

Siegkas, Petros. "INTRODUCTION TO INDUSTRIAL DESIGN AND PRODUCT CASE STUDIES." The 17th International CDIO Conference

Ahmed Tamkin, Siegkas Petros. “INTEGRATED CAD AND REVERSE ENGINEERING TO ENHANCE CONCEPTION AND DESIGN”. The 17th International CDIO Conference

Siegkas, Petros. "REVERSE ENGINEERING AND INTRODUCTION TO ENGINEERING DESIGN." The 16th International CDIO Conference. Vol. 2.

Indicative project examples:

Porous-Cellular Materials

Dr Siegkas has worked in combining the capabilities of generative machine learning with 3D printing and material science. Generative adversarial network (GAN) was used to extract and transfer complex structure features from x-ray microtomography images of porous and lattice structures. The study used a two-dimensional GAN to generate images of cellular materials, which were then transformed into 3D structures and manufactured using 3D printing. The compressive behavior of the 3D printed structures was compared to the original structures. The study has potential for biomimicking and generating hybrid natural-artificial structures.

Dr Siegkas’ has worked on developing experiments and modeling techniques to characterise and model the static and dynamic performance of porous- cellular materials for use as biocompatible bone implants, and applications within the aerospace industry. Experiments involved mechanical testing at various strain rates and included the use of high speed photography, in-situ SEM, X-ray micro-tomography and image analysis. A computational method was developed for virtually generating porous material prototypes and capturing their mechanical behaviour at different scales thus providing a tool for virtual prototyping and characterisation, based on application requirements. The foam geometries were generated in Matlab using a method based on Voronoi polyhedrals, that could either mimic geometrical features (e.g. pore size distribution) obtained by X-ray tomography analysis or generate new prototype geometries. The geometries were then subjected to uniaxial and multiaxial mechanical loading at the mesoscale level from which the macroscale properties were obtained. The virtual specimens were simulated using finite element techniques (Abaqus) at different strain rates. The project was partially funded by Rolls Royce, EPSRC and in collaboration with the IMI Canada. The computational method algorithms were then further developed and used in generating 3D printed porous materials and composites. The structures are either porous of a single material or composites of two materials that are geometrically intertwined. The method is used to produce and explore promising material combinations that could otherwise be difficult to mix. It is potentially applicable with a variety of additive manufacturing methods, size scales, and materials for a range of potential applications.

Metal Alloys-Ballistics-Aerospace Components

Dr Siegkas worked on the ballistic performance of granular materials (including Nickel at high temperatures up to 800 degrees Celsius and Titanium alloys at room temperature) and jet engine components. The research was related to aviation safety and impact energy management (i.e. jet engine fan blade off e.t.c.). The projects were focused in designing experiments and generating data for material model calibration and validation, or used for the development and optimisation of new materials. The aim was to capture the material mechanisms involved in managing energy dissipation and related to application requirements. The projects were in collaboration with industrial partners (Rolls Royce, TIMET) and the work has received a departmental award under the University of Oxford departmental recognition scheme. Results were subject to publishing restrictions.

Traumatic Brain Injury and Protective Equipment

Dr Siegkas worked on projects related to understanding and preventing traumatic brain injury. The work involved implementing finite element models and extracting information as to regions of the brain that were mechanically affected by impact loading. Part of the work was funded by the ‘’Welcome trust network of excellence’’ and was carried by a multidisciplinary team from the fields of biology, neuroscience and solid mechanics. Other parts of the work were funded by the Welsh Government and in a collaboration between Imperial College and industrial partners (Dainese-AGV manufacturer of motorcycle helmets, Rheon labs manufacturer of liner add-on material). Dr Siegkas' role involved both experiments and computational modelling. Experiments included liner material characterisation (polymer), drop tower experiments on helmets (with and without added liner) and fitted on an instrumented head form. Modelling included using a head and brain model to extract information regarding potential injury and a full helmet-head form (Hybrid III) model of the drop tower tests aiming to extract information for further liner design and optimisation.

Funding bodies

Undertaken projects were funded in collaboration with industrial, public and academic partners with expertise from multiple disciplines including: Rolls Royce, TIMET, Dainese, Rheon labs, Bridgestone (Japan), Division of Brain sciences Imperial College London,  EPSRC, Welsh Government and Welcome Trust Network of Excellence.