ArticleMaterials (Basel, Switzerland)2025
Assessment and Comparison of Phenomenological and Physical Constitutive Models for Predicting the Hot Deformation Behavior of Metallic Materials: A Pathway for Sustainable Metal Forming in Al-Kharj Governorate.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Constitutive Characterization of FeCoCrNi High-Entropy Alloy During Thermomechanical Deformation Using a New Zerilli-Armstrong Model.Materials (Basel, Switzerland) · 2026Article
- Advanced Materials, Machinability and Intelligent Manufacturing Systems.Materials (Basel, Switzerland) · 2026Article
- Advanced Constitutive Modeling of the Hot Deformation Behavior of Ni-Based Superalloys: Modified Kobayashi-Dodd and Khan-Huang-Liang Models with Experimental Validation.Materials (Basel, Switzerland) · 2025Article
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2 authors.
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Abstract
In the context of Al-Kharj city, which is steadily advancing as an industrial and manufacturing hub within Saudi Arabia, this study has significant relevance. The city's focus on metal forming, fabrication, and materials engineering makes it crucial to optimize processes such as hot deformation of metallic alloys for various sectors, including aerospace, automotive, oil and gas, and structural applications. By assessing and comparing phenomenological and physical material models for nickel, aluminum, titanium, and iron-based alloys, this study aids Al-Kharj industries in advancing their process simulation and predictive performance. Thus, this study aims to evaluate the proposed phenomenological and physically based constitutive models for Ni-, Al-, Ti-, and Fe-based alloys to enhance the accuracy of high-temperature deformation simulations. Phenomenological models investigated include the Johnson-Cook (JC), Fields and Backofen (FB), and Khan-Huang-Liang (KHL) formulations, while the Zerilli-Armstrong (ZA) model represents the physical category. Additionally, various modifications to these models are explored. Model parameters are calibrated using the Levenberg-Marquardt algorithm to minimize mean square error. Performance is assessed through key statistical metrics, including the correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE). Of the 32 models analyzed, a modified version of the JC model delivers the highest accuracy across all alloys. Furthermore, four other modifications, one each for the JC and ZA models and two for the FB model, exhibit superior predictive capability for specific alloys. This makes this study valuable not just academically, but also as a practical resource to boost Al-Kharj's industrial competitiveness and innovation capacity.
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