Load-bearing structures are typically designed towards relevant load cases assuming static shape and fixed sets of materials properties decided upon during design and materials selection. Structures that could change local properties in service in response to load change could raise additional weight saving potentials , thus supporting lightweight design and sustainability.
Materials with such capabilities must necessarily be composite in the sense of a heterogeneous build-up, exhibiting e. g. an architecture consisting of numerous active cells with sensing, signal and data processing and actuation/stimulation capability.
One concern regarding active smart cellular structures is correlated control of cells’ responses, and the underlying informational organization providing robustness and real-time capabilities. We suggest a two-stage approach which combines machine learning with mobile and reactive Multi-agent Systems (MAS). In it, the MAS’ task is to analyse loading situations based on sensor data and negotiate matching spatial redistributions of material properties like elastic modulus to achieve higher-level optimization aims like a minimum of the total strain energy within the structure, or a reduction of peak stress levels. The associated machine learning approach would be employed to recognise loading situations already encountered in the past for which optimized solutions exist and in such cases bypass the MAS system to directly enforce the respective property distribution.
In the present study, a proof of concept of the approach is presented which combines finite element method (FEM) and MAS simulation, with the former primarily taking the place of the physical structure. In addition, FEM simulations are used for off-line training of the MAS prior to its deployment in the real or simulated structure. The classification models learned this way represent a starting point which is constantly being updated at run-time during the service life of the structure using incremental learning techniques.