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Meeting Materials Science & Technology 2020
Symposium Multiscale modeling of plasticity and fracture in superalloys: A statistical and data science perspective
Organizer(s) Stefanos Papanikolaou, West Virginia University
Terence Musho, West Virginia University
Jaimie Tiley, AFOSR Office Of Scientific Research (AFOSR/RTA)
Scope Predicting mechanical failure in superalloys (Ni-based, Ti-based etc.) represents a fantastic challenge as it depends on a myriad of factors related to material microstructure, specimen geometry and loading conditions. Fracture mechanics and plasticity theory provide the quantities that control plastic flow and failure at the continuum scale. But it does not give any hint on the microscopic quantities affecting the strength and toughness of materials. Micromechanical approaches try to bridge microstructural features of solids with their mechanical response, but they are confronted to the high level of complexity of microstructures and microscopic damage processes involved in materials. Recently, new approaches inspired from data science have emerged: the key microscale parameters that control fracture at large scale can be identified, and even learned, from the statistical analysis of a large amount of data. The objective of this symposium is to gather scientists from the Mechanical Engineering, Physics and Materials Science communities interested by data science approaches in failure and plasticity to discuss the recent advances in this rapidly growing field and promote inter-disciplinary interactions.

The proposed symposium is timely for several reasons. On one side, multiscale mechanics models integrate now more and more the stochastic nature of the fracture and plasticity processes that result from the microstructural disorder of materials [1,2]. This allows to go beyond the average response of solids and thus provides insights on the fluctuations and their statistical features. These fluctuations are evidenced on quantities like the rate of plastic flow, the crack length or the morphologies that result from fracture and plasticity [3-6]. In return, the improved understanding of these signals and patterns open perspectives for the development of new experimental techniques that consist in extracting meaningful, and often precious, information from the statistical treatments of fluctuations observed during fracture and plasticity. On the other side, tools borrowed to data science (like data mining or machine learning) have been increasingly used in the field of solid mechanics, very often in conjunction with multi-scale models of fracture and plasticity, building a real synergy between both approaches [7]. Currently, statistical predictions range in a wide gamut of applications, from indentation of composites [8] to neural networks for understanding of elastic and plastic [9, 10] features of deformation in crystals.

Overall, the interplay between data science and mechanics have provided new venues for the design of materials with improved properties and paved the way for new methods for the multi-scale characterization of the mechanical properties of materials that will be discussed during the symposium. We envision that this workshop will contribute to create a new multi-disciplinary community that will sustain the development of this rapidly growing research field.

[1] S. Zapperi, Current challenge for statistical physics in fracture and plasticity, Eur. Phys. J. B 85, 329 (2012).
[2] L. Ponson, Statistical aspects in crack growth phenomena: How the fluctuations reveal the failure mechanisms, Int. J. Frac. 201, 11-27 (2016).
[3] S. Vernède, L. Ponson & J. P. Bouchaud, Turbulent fracture surfaces : A footprint of damage percolation ? Phys. Rev. Lett. 114, 215501 (2015).
[4] A. Srivastava, L. Ponson, S. Osovski, E. Bouchaud, V. Tvergaard & A. Needleman, Effect of inclusion density on ductile fracture toughness and roughness, J. Mech. Phys. Solids 63, 62 (2019).
[5] A. Hinkle, W. Nohring and L. Pastewka, The universal emergence of self-affine roughness from deformation, ArXiv:1901.03236 (2019).
[6] J. Barès, D. Bonamy & A. Rosso, Seismic-like organization of avalanches in a driven long-range elastic string as a paradigm of brittle cracks, ArXiv:1902.05758
[7] S. Papanikolaou, Y. Cui and N. Ghoniem, Avalanches and plastic flow in crystal plasticity: an overview. Model. Simul. Mater. Sci. Eng. 26, 013001 (2017).
[8] J. H. Panchal, S. R. Kalidindi, and D. L. McDowell, Key computational modeling issues in integrated computational materials engineering, Computer-Aided Design 45, no. 1, 4-25, (2013).
[9] C. Settgast, M. Abendroth, and M. Kuna, Constitutive modeling of plastic deformation behavior of open-cell foam structures using neural networks, Mechanics of Materials (2019).
[10] H. Salmenjoki, M. J. Alava, and L. Laurson. Machine learning plastic deformation of crystals, Nature communications 9, no. 1 (2018): 5307.
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