Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates

Computing and Communications

Text available via DOI:

https://doi.org/10.3390/ma16155381
Final published version
Available under license: CC BY: Creative Commons Attribution 4.0 International License

Keywords

artificial intelligence, bistable, composite, correlation, machine learning, SHAP, snap-through, XGBoost

View graph of relations

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates. / Saberi, Saeid; Nasiri, Hamid; Ghorbani, Omid et al.
In: Materials, Vol. 16, No. 15, 5381, 31.07.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{14d6f357160e41209260e352032c44a5,

title = "Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates",

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{\textquoteright}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.",

keywords = "artificial intelligence, bistable, composite, correlation, machine learning, SHAP, snap-through, XGBoost",

author = "Saeid Saberi and Hamid Nasiri and Omid Ghorbani and Friswell, {Michael I.} and Castro, {Saullo G.P.}",

note = "Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

month = jul,

day = "31",

doi = "10.3390/ma16155381",

language = "English",

volume = "16",

journal = "Materials",

issn = "1996-1944",

publisher = "MDPI AG",

number = "15",

}

RIS

TY - JOUR

T1 - Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates

AU - Saberi, Saeid

AU - Nasiri, Hamid

AU - Ghorbani, Omid

AU - Friswell, Michael I.

AU - Castro, Saullo G.P.

PY - 2023/7/31

Y1 - 2023/7/31

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - bistable

KW - composite

KW - correlation

KW - machine learning

KW - SHAP

KW - snap-through

KW - XGBoost

U2 - 10.3390/ma16155381

DO - 10.3390/ma16155381

M3 - Journal article

C2 - 37570085

AN - SCOPUS:85167779578

VL - 16

JO - Materials

JF - Materials

SN - 1996-1944

IS - 15

M1 - 5381

ER -

Research

Links

Text available via DOI:

Keywords