Virtual Modelling Framework-Based Inverse Study for the Mechanical Metamaterials with Material Nonlinearity

Mechanical metamaterials have become a critical research focus across various engineering fields. Recent advancements have pushed the development of reprogrammable mechanical metamaterials to achieve adaptive mechanical behaviours against external stimuli. The relevant designs strongly depend on a thorough understanding of the response spectrum of the original structure, where establishing an accurate virtual model is regarded as the most efficient approach to this end up to now. By employing an extended support vector regression (X-SVR), a powerful machine learning algorithm model, this study explores the uncertainty and sensitivity analysis and inverse study of re-entrant honeycombs under quasi-static compressive loads. The proposed framework enables accurate uncertainty quantification, sensitivity analysis, and inverse study, facilitating the related design and optimisation of metastructures when extended to responsive materials. The proposed framework is considered an effective tool for uncertainty quantification and sensitivity analysis, enabling the identification of key parameters affecting mechanical performance. Finally, the inverse study approach leverages X-SVR to swiftly obtain the required structural configurations based on targeted mechanical responses.