Titanium alloys are widely preferred in the healthcare sector as biocompatible materials due to their superior properties such as low density and exceptional mechanical strength. Their low density provides lightweight solutions, and their density is closer to that of human bone compared to other metallic alloys with similar strength. This similarity facilitates a balanced load distribution between the bone and the implant, enhancing biomechanical compatibility. This study investigates the effects of alloying elements on the density of titanium-based biomedical materials using a computational materials science approach. A total of 72 different compositions of Ti-Al-V alloys were modeled using JMatPro software, and their densities were simulated at room temperature (25°C). The simulation produced a comprehensive dataset, which was utilized to train an explainable artificial intelligence (XAI) model. Advanced interpretability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP), were employed to elucidate the influence of each alloying element on the density. The dataset was analyzed using an XAI-based regression model implemented with the Artificial Neural Network (ANN) algorithm. The interpretability graphs provided insights into the individual contributions of the alloying elements, revealing their positive or negative effects on the density. The findings offer a deeper understanding of the role of alloying elements in optimizing the performance of titanium-based biomedical materials, particularly in achieving lightweight designs. This study highlights the potential of integrating computational material modeling with explainable AI to advance the design and development of high-performance lightweight materials for biomedical applications.

Artificial intelligence-driven prediction models have emerged as powerful tools for estimating material properties with high accuracy, yet the preparation of training datasets often demands labor-intensive and time-consuming experimental procedures. Leveraging the Computational Materials Science (CMS) approach, this study utilizes phase transformation calculations and thermodynamic data to simulate the mechanical properties of Ni-Cr-Fe alloys. Using JMatPro software, mechanical properties (0.2% Proof Stress, Fracture Stress, and Young's Modulus) of 50 Ni-Cr-Fe alloy compositions were simulated across a temperature range of 540–920°C, generating a dataset of 1000 rows. This dataset was used to train an Artificial Neural Network (ANN) model, with 80% allocated for training and 20% for validation and testing. The trained AI model demonstrated robust predictive capabilities, achieving a 96,61% accuracy rate in forecasting material compositions with the desired thermo-physical properties at specific temperatures. To validate the model's reliability, predicted alloy compositions were re-simulated under identical conditions in JMatPro, confirming the high fidelity of the model's predictions. The results underscore the efficacy of Computational Materials Science (CMS)-generated datasets as a scalable and reliable source for training AI models in materials science. This study highlights the potential of integrating Computational Materials Science (CMS) and Machine Learning approaches to accelerate material design and development processes, delivering significant improvements in prediction speed and accuracy.

This study investigates the impact of alloying elements on the mechanical properties of Ni-Cr-Fe-based superalloys using a computational materials science approach. Fifty different compositions of superalloys, commonly known as Inconel and Incoloy, were modeled using the JMatPro software. The mechanical properties, including 0.2% proof stress (MPa), fracture stress (MPa), and Young’s modulus (GPa), were simulated across a temperature range from 540°C to 920°C at 20°C intervals for each alloy. The simulation generated a comprehensive dataset comprising 1000 rows. This dataset was then utilized to train an explainable artificial intelligence (XAI) model, leveraging advanced techniques such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Partial Dependence Plots (PDP). The dataset was analyzed using an XAI-based regression model employing the XGBoost algorithm. The interpretability graphs were analyzed to evaluate the individual contributions of each alloying element to the mechanical properties over the entire temperature range. The findings provide detailed insights into the positive or negative effects of alloying elements, enabling a better understanding of their role in optimizing superalloy performance under various thermal conditions. This work highlights the potential of integrating computational materials modeling and explainable AI to advance the design and development of high-performance materials.

Bu çalışmada, polipropilen (PP)/poliamid 6 (PA6) karışımlarında kullanılan kemik külü ve uyumlaştırıcı olarak maleik anhidrit aşılı polipropilen (MAPP) katkısının kompozitlerin mekanik ve ayrıca tribolojik özellikleri üzerindeki etkileri araştırılmıştır. Kemik tozu özellikle gıda sektöründe atık bir malzeme olarak karşımıza çıkmaktadır. Polimer matrisli kompozitlerde değerlendirilmesi petrol bazlı içeriğin azaltılması ve biyo-bozunur malzeme avantajı sunmasından dolayı önem arz etmektedir. Kompozitler ergiyik harmanlama yöntemi ile bir çift vidalı ekstrüder kullanılarak üretilmiş ve ardından enjeksiyon kalıplama ile mekanik ve tribolojik testlerde kullanılacak numune ölçülerine kalıplanmışlardır. Kompozitlere çekme testi, üç-nokta eğme testi, Shore D sertlik testi ve ball-on-disk kayma aşınma testleri uygulanmış ve sonuçlar kompozit bileşimine göre karşılaştırılmıştır. Sonuçlar incelendiğinde, kemik külü katkısının sertlik üzerinde, uyumlaştırıcı katkısının ise çekme ve eğme mukavemeti üzerinde daha etkili olduğu bulunmuştur. %20 ve %30 kemik külü katkısında en yüksek mekanik özelliklere ulaşılmıştır. Kemik külünün sürtünme katsayılarını düşürmede de çok etkili olduğu, özellikle uyumlaştırıcı ile birlikte kullanıldığında aşınma direncini önemli miktarda (%98’e kadar) azalttığı bulunmuştur. Özetle, kemik külü ve MAPP sinerjik etkisi ile elde edilen PP/PA6 matrisli kompozitler mekanik ve aşınma direnci gerektiren uygulamalar için ümit vadetmektedir.

The CALPHAD methodology models the physical, mechanical, and thermodynamic properties of materials based on specified alloy compositions using phase equilibrium calculations and thermodynamic databases. With the CALPHAD approach, millions of material-property data can be obtained for each alloy composition over various temperature ranges. However, finding an alloy with the desired properties often requires lengthy trial-and-error processes that involve manually adjusting the composition. In this study, the goal is to inverse this approach using artificial intelligence to predict alloy compositions that yield the desired properties. Accordingly, in the JMatPro software based on the CALPHAD methodology, the physical properties (density, thermal conductivity, linear expansion, Young's modulus, bulk modulus, shear modulus, and Poisson's ratio) of 250 different Ni-Cr-Fe-based superalloys in the temperature range of 540–920 °C were modeled. A dataset with 5000 rows was created from the generated data, of which 80% was used to train the artificial intelligence model, while 20% was reserved for validation and testing. Through analyses using Explainable Artificial Intelligence (XAI) and Artificial Neural Networks (ANN), alloy compositions with the desired physical properties at a given temperature were predicted with a high accuracy rate of 98.03%. In conclusion, beyond obtaining material properties from alloy compositions through the CALPHAD approach, artificial intelligence techniques make it possible to accurately inverse predict alloy compositions that yield specified physical properties at a particular temperature.