Machine Learning Prediction of Avalanche-like Events in Knitted Fabric

Knitted fabric is a thread-based metamaterial that exhibits crackling noise when deformed. Discrete events of broadly-distributed sizes are displayed both in the force signal and in the deformation field. The occurrence of those intermittent, scale-invariant events cannot be accurately predicted at the time being, as in most systems with similar avalanche-like behavior. However, Machine Learning methods have proven to be a useful tool when dealing with time-series predictions and image processing, giving hope to forecast fault failures in our system. We study the feasibility of predicting quantities such as next event amplitude or fault failure times by training a Neural Network algorithm to infer information from a series of past force signal and deformation field. Furthermore, since knits display spatially extended avalanche-like yielding events, we studied their statistical properties and compared them to other known systems to assess whether they can be considered an analogous seismic model.
Crackling Dynamics in the Mechanical Response of Knitted Fabrics, Samuel Poincloux, Mokhtar Adda-Bedia and Frédéric Lechenault, Physical Review Letters, vol.30, 2018.