Prediction of Creep Life Using an Explainable Artificial Intelligence Technique and Alloy Design Based on the Genetic Algorithm in Creep-Strength-Enhanced Ferritic 9% Cr Steel

Kong, Byeong Seo; Kim, Min Soo; Kim, Byung Hoon; Lee, Je Hyun

doi:10.1007/s12540-022-01312-7

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…GAs have been extensively applied in job scheduling [19,27,[30][31][32]. However, most GA applications have not been well explained [33].…”

Section: Existing Xai Techniques For Explaining Ga Applications In Jo...mentioning

confidence: 99%

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Novel XAI techniques for explaining GA applications in job scheduling

Chen¹,

Wang

2022

Preprint

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Many evolutionary artificial intelligence (AI) technologies have been applied to assist job scheduling in manufacturing. Among them, genetic algorithms (GAs) are one of mainstream methods. However, GA applications in this field may not be easy to understand or communicate, especially to factory workers without relevant background knowledge, preventing widespread acceptance of such applications. To address this problem, the concept of explanatory AI (XAI) has been proposed. This study first reviews existing XAI techniques for explaining GA applications in job scheduling. Based on the review results, the problems faced by existing XAI techniques are summarized. To solve these problems, this study proposes several novel XAI techniques, including decision tree-based interpretation, dynamic transformation and contribution diagrams, and improved bar charts. To illustrate the effectiveness of the proposed methodology, it has been applied to a case in the literature. According to the experimental results, the proposed methodology can make up for the deficiencies of existing XAI methods in processing high-dimensional data and visualizing the contribution of feasible solutions, thereby satisfying all the requirements for an effective XAI technique for explaining GA applications in job scheduling. Furthermore, the proposed methodology can be easily extended to explain other evolutionary AI applications such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC) in job scheduling.

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“…GAs have been extensively applied in job scheduling [19,27,[30][31][32]. However, most GA applications have not been well explained [33].…”

Section: Existing Xai Techniques For Explaining Ga Applications In Jo...mentioning

confidence: 99%

“…fuzzy inference rules (or systems), SHAP [27], local interpretable model-agnostic explanation (LIME) [40], etc., are not suitable for explaining GA applications in job scheduling.…”

Section: Problems Of Existing Xai Techniquesmentioning

confidence: 99%

Novel XAI techniques for explaining GA applications in job scheduling

Chen¹,

Wang

2022

Preprint

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“…They predicted the creep rupture life of Ni-based single-crystal superalloys and investigated the effect of microstructure on the creep properties of Ni-based single-crystal superalloys. Kong et al [31] optimized the machine learning model using a genetic algorithm. These authors then predicted the creep rupture life of 9% Cr alloy and studied the relationship between the composition and creep properties of 9% Cr alloy.…”

Section: Introductionmentioning

confidence: 99%

A Method for Predicting the Creep Rupture Life of Small-Sample Materials Based on Parametric Models and Machine Learning Models

Zhang,

Yao,

et al. 2023

Materials

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In view of the differences in the applicability and prediction ability of different creep rupture life prediction models, we propose a creep rupture life prediction method in this paper. Various time–temperature parametric models, machine learning models, and a new method combining time–temperature parametric models with machine learning models are used to predict the creep rupture life of a small-sample material. The prediction accuracy of each model is quantitatively compared using model evaluation indicators (RMSE, MAPE, R2), and the output values of the most accurate model are used as the output values of the prediction method. The prediction method not only improves the applicability and accuracy of creep rupture life predictions but also quantifies the influence of each input variable on creep rupture life through the machine learning model. A new method is proposed in order to effectively take advantage of both advanced machine learning models and classical time–temperature parametric models. Parametric equations of creep rupture life, stress, and temperature are obtained using different time–temperature parametric models; then, creep rupture life data, obtained via equations under other temperature and stress conditions, are used to expand the training set data of different machine learning models. By expanding the data of different intervals, the problem of the low accuracy of the machine learning model for the small-sample material is solved.

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Explainable Artificial Intelligence (XAI) in Manufacturing

Chen

2023

SpringerBriefs in Applied Sciences and Technology

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Prediction of Creep Life Using an Explainable Artificial Intelligence Technique and Alloy Design Based on the Genetic Algorithm in Creep-Strength-Enhanced Ferritic 9% Cr Steel

Cited by 15 publications

References 27 publications

Novel XAI techniques for explaining GA applications in job scheduling

Novel XAI techniques for explaining GA applications in job scheduling

A Method for Predicting the Creep Rupture Life of Small-Sample Materials Based on Parametric Models and Machine Learning Models

Explainable Artificial Intelligence (XAI) in Manufacturing

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