Jul 27 2023


1:30 pm - 3:00 pm

Computational Alloy Design for Structural Applications

Dr Marjorie Cavarroc and Dr Edern Menou,

Safran Tech, 78117 Châteaufort, France



Dr Marjorie Cavarroc is a senior Research & Technology engineer at Safran Tech, Safran Group corporate research center. She is an expert in dry surface treatments and plasma processes. She is a French engineer who defended her PhD thesis in 2004, and her French “Habilitation à Diriger des Recherches” in 2020. She has a long experience in the correlation between process parameters, textures, microstructures and properties of functional materials. She first was CTO of a small company dedicated to technology transfer, before joining Safran in 2013. Her current interests range from the search for innovative multifunctional materials to the development of green processes to elaborate nanostructured materials, for applications in energy, aerospace and aeronautical industries. She received several awards all along her professional carreer (Scientific and technic vocation award in 1998, Engineer of the year award in 2010, Irène Joliot-Curie award in 2022). She belongs to the Women of Tech network of the French Technology Academy. She has co-authored 25 peer-reviewed articles, 64 oral presentations, 8 invited conferences, and 23 patents.

Dr Edern Menou is a Research & Technology engineer at Safran Tech, Safran Group corporate research center. He holds an engineering degree in materials science with a specialisation in welding metallurgy, and is a certified International Welding Engineer. His PhD thesis (2016) consisted in developping a computational alloy design methodology comprising physics- and machine learning-based modelling, multiobjective optimisation and multicriteria decision analysis for the design of nickel-base superalloys and high entropy alloys. He joined Safran Tech in 2017 to pursue these developments and enlarge their application to nickel-based single-crystal superalloys and titanium- and aluminium-based alloys for applications in energy, aerospace and aeronautical industries. His current interests also include Deep Learning for Computer Vision or Natural Language Processing generally aimed at novel materials discovery. He co-authored 18 patents and 8 peer-reviewed articles.


For decades, alloy design has been central to yielding technological improvements in structural applications, including in the aerospace industry. New high performance alloys are much needed as material requirements, regulations and socio-economic expectations tighten. This talk will focus on work conducted at Safran Tech, Safran Group corporate research center, on the development on new metallic alloys for gas turbines applications. Such complex applications embody many aspects of the usual compromises in material design: alloys are subject to wide temperature ranges, a variety of severe mechanical loads, and corrosive environments, the combination of the former three factors leading to an increasingly complex service life. In response, alloy developments are conducted on nickel-, titanium-, iron- and aluminium-based alloys, each of which have diverse capabilities and thus, uses. While regulations tighten, improvements made during the last few decades in numerical modelling and hardware allow for much broader design considerations. In the field of material sciences, concepts advocated through the Material Genome Initiative or Integrated Computational Material Engineering are meant to make use of such advancements, by proposing the integration of as much multi-scale, multi-physics modelling as possible to accelerate the introduction in service of novel materials. Hardware improvements also lead to the popularisation of Machine Learning, and more recently Deep Learning methods, which are useful to complement classical modelling approaches.

This talk will introduce the problem of alloy design as well as accelerated alloy development and experimental validation from the perspective of an engine manufacturer, and show through several use cases how a a relatively simple, integrated ML-aided methodology can lead to innovative materials with original combinations of properties in a relatively short span of time. The many limitations of such system, the foremost issue being data availability, will also be discussed, as will prospective methodological enhancements and applications of Deep Learning in alloy design.

Finally, a discussion of preliminary results in numerical coating design using the same basis will also be held.