Print Email Facebook Twitter Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates Title Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates Author Saberi, Saeid (Isfahan University of Technology) Nasiri, Hamid (Amirkabir University of Technology) Ghorbani, Omid (Kharazmi University) Friswell, Michael I. (Swansea University) Castro, Saullo G.P. (TU Delft Aerospace Structures & Computational Mechanics) Date 2023 Abstract Material properties, geometrical dimensions, and environmental conditions can greatly influence the characteristics of bistable composite laminates. In the current work, to understand how each input feature contributes to the curvatures of the stable equilibrium shapes of bistable laminates and the snap-through force to change these configurations, the correlation between these inputs and outputs is studied using a novel explainable artificial intelligence (XAI) approach called SHapley Additive exPlanations (SHAP). SHAP is employed to explain the contribution and importance of the features influencing the curvatures and the snap-through force since XAI models change the data into a form that is more convenient for users to understand and interpret. The principle of minimum energy and the Rayleigh–Ritz method is applied to obtain the responses of the bistable laminates used as the input datasets in SHAP. SHAP effectively evaluates the importance of the input variables to the parameters. The results show that the transverse thermal expansion coefficient and moisture variation have the most impact on the model’s output for the transverse curvatures and snap-through force. The eXtreme Gradient Boosting (XGBoost) and Finite Element (FM) methods are also employed to identify the feature importance and validate the theoretical approach, respectively. Subject compositebistableartificial intelligencemachine learningsnap-throughcorrelationSHAPXGBoost To reference this document use: http://resolver.tudelft.nl/uuid:0ba2930d-ed46-427f-9592-b570a945c960 DOI https://doi.org/10.3390/ma16155381 ISSN 1996-1944 Source Materials, 16 (15) Part of collection Institutional Repository Document type journal article Rights © 2023 Saeid Saberi, Hamid Nasiri, Omid Ghorbani, Michael I. Friswell, Saullo G.P. Castro Files PDF materials_16_05381.pdf 7.25 MB Close viewer /islandora/object/uuid:0ba2930d-ed46-427f-9592-b570a945c960/datastream/OBJ/view