Multiscale Modelling and Characterization of Coupled Damage-Healing For Materials in Concurrent Computational Homogenization Approach Using Machine Learning

Abstract

The multiscale modelling and characterization of coupled damage-healing phenomena in materials play a crucial role in understanding and predicting the behaviour of complex material systems. In this study, we propose a concurrent computational homogenization approach combined with machine learning techniques to model and characterize the coupled damage-healing process. The objective of this research is to develop an efficient and accurate methodology that can capture the intricate interactions between damage evolution and healing mechanisms at multiple scales. By integrating machine learning algorithms into the computational homogenization framework, we aim to enhance the predictive capabilities and computational efficiency of the modelling approach. The significance of this lies in its potential to provide valuable insights into the damage-healing behaviour of materials, which can aid in the development of advanced materials with enhanced durability and longevity. Furthermore, the proposed methodology has the potential to accelerate the design and optimization processes for engineering structures by providing accurate predictions of material response under varying loading conditions.

To achieve these objectives, we will review the existing literature on multiscale modelling, damage mechanics, healing mechanisms, and machine learning techniques. This literature review will serve as the foundation for developing the methodology. We will also investigate previous studies that have utilized machine learning in the context of material damage and healing to gain insights into the potential advantages and limitations of incorporating machine learning into the concurrent computational homogenization approach. The evaluation of the proposed methodology will be conducted through extensive numerical simulations and comparison with experimental results. Various metrics, such as damage evolution accuracy, healing efficiency, and computational efficiency, will be employed to assess the performance of the approach.

The outcomes of this research will provide a deeper understanding of the coupled damage-healing process in materials and establish a foundation for further advancements in multiscale modelling and characterization. The application of machine learning techniques in concurrent computational homogenization has the potential to revolutionize the field of materials science and engineering by enabling more accurate predictions and efficient design of materials with enhanced damage tolerance and self-healing capabilities.