AIMC Topic: Alloys

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Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties.

PloS one
Understanding and controlling grain growth kinetics in steels is crucial for optimizing mechanical properties during thermomechanical processing. However, traditional empirical models often fail to account for the complex, nonlinear interactions betw...

Predicting and Analyzing Nitrate Adsorption on High-Entropy Alloys Based on Pair Distribution Function Using a Hybrid Machine Learning Framework.

Journal of chemical information and modeling
Incorporating five or more metals into a single structure creates a new family of alloys, high-entropy alloys (HEAs), which hold several exceptional properties, such as outstanding stabilities and continuous electronic structures, making them promisi...

A collaborative approach of finite element method and machine learning algorithms for biomechanical analysis of implants used in tibial shaft fractures.

BMC musculoskeletal disorders
BACKGROUND: Tibial fractures are among the most common complex orthopedic injuries. The mechanical strength and biomaterial properties of implants used in the treatment of such fractures directly affect the healing process. In this study, the mechani...

Predicting the tensile properties of heat treated and non-heat treated LPBFed AlSi10Mg alloy using machine learning regression algorithms.

PloS one
In this study, the ability of machine learning algorithms to predict tensile properties of both heat-treated and non-heat treated LPBFed AlSi10Mg alloy is investigated. The data was analyzed using various Machine Learning Regression (MLR) models such...

Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy.

PloS one
Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approac...

Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al-Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation.

Journal of chemical information and modeling
Understanding the tensile mechanical properties of Al-Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuable insights. However, these simulations are constrained by the unavailability of suitable interatomi...

Rapid and Differential Diagnosis of Sepsis Stages Using an Advanced 3D Plasmonic Bimetallic Alloy Nanoarchitecture-Based SERS Biosensor Combined with Machine Learning for Multiple Analyte Identification.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Rapid and accurate differential diagnosis of infections, sepsis, and septic shock is essential for preventing unnecessary antibiotic overuse and improving the chance of patient survival. To address this, a 3D gold nanogranule decorated gold-silver al...

Deep learning assisted prediction of osteogenic capability of orthopedic implant surfaces based on early cell morphology.

Acta biomaterialia
The surface modification of titanium (Ti) and its alloys is crucial for improving their osteogenic capability, as their bio-inert nature limits effective osseointegration despite their prevalent use in orthopedic implants. However, these modification...

MGT: Machine Learning Accelerates Performance Prediction of Alloy Catalytic Materials.

Journal of chemical information and modeling
The application of deep learning technology in the field of materials science provides a new method for predicting the adsorption energy of high-performance alloy catalysts in hydrogen evolution reactions and material discovery. The activity and sele...

Bioinspired design and validation of a soft robotic end-effector with integrated shape memory alloy-driven suction capabilities.

Bioinspiration & biomimetics
The exploration of adaptive robotic systems capable of performing complex tasks in unstructured environments, such as underwater salvage operations, presents a significant challenge. Traditional rigid grippers often struggle with adaptability, wherea...