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Papers
61,005 resultsShowing papers similar to Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach
ClearApplication of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys
Researchers used machine learning to analyze what factors influence the formation of twin structures in magnesium alloys, studying over 3,000 individual grains. They found that grain boundary characteristics and loading conditions were the most important predictors of twin nucleation. The study demonstrates that machine learning can be a powerful tool for understanding complex microstructural behavior in metals.
Simulation of recoverable strain variation during isothermal holding of the Ni51Ti49 alloy under various regimes
Researchers modeled the strain behavior of a nickel-titanium shape memory alloy during isothermal holding at various stress levels. The study used a microstructural model and optimization algorithm to fit strain variation curves. Shape memory alloys are relevant to materials science research but not directly connected to microplastic pollution.
Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges
This review covers machine learning methods applied to predicting and understanding mechanical properties of materials from large datasets. It is an engineering informatics paper and is not related to microplastics or environmental health.
A data-driven approach for the assessment of the thermal stratification of reservoirs based on readily available data
Researchers used a data-driven machine learning approach to assess the thermal structural integrity of materials under variable conditions, providing predictive models that can reduce reliance on costly physical testing. The methodology has broader applications for materials used in environments with high thermal stress.
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
Researchers used machine learning and Bayesian network analysis on 4D microscopy data from cracking metal samples to identify which microstructural features best predict how small fatigue cracks grow and in which direction. The resulting analytical model outperformed existing fatigue metrics, offering a more accurate tool for predicting when and how structural metal components will fail under repeated stress.
A damage-based uniaxial fatigue life prediction method for metallic materials
Researchers developed a faster method for determining how long metal components will last under repeated stress by tracking tiny changes in material stiffness as damage accumulates, rather than running tests until failure. The method was validated across ten different metals including steel, aluminum, and titanium, consistently matching results from standard but much more time-consuming tests.
Structure Refinement and Bauschinger Effect in fcc and hcp Metals
This study investigated how varying initial microstructure and aging-induced changes in a nickel-rich Ti-Ni shape memory alloy affect its Young's modulus, relevant to designing low-modulus bone implants that better match natural bone stiffness.
A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure–Property Relationships of Nanoporous Metals
This materials science study applied machine learning to predict the mechanical properties of nanoporous metals from their microstructural features, offering an efficient way to optimize material design. While focused on metals rather than plastics, similar data-driven approaches are being developed for predicting the environmental behavior of microplastics.
Comparative study of elastic properties measurement techniques during plastic deformation of aluminum, magnesium, and titanium alloys: application to springback simulation
Researchers compared multiple tensile test-based protocols for measuring changes in elastic moduli of aluminium, magnesium, and titanium alloys during plastic deformation, addressing discrepancies between existing loading-unloading evaluation methods. The study aimed to provide more reliable elastic property measurements to improve springback simulation accuracy in metal forming applications.
Microstructure-Specific Lifetime Prediction Method for Heavy-Section Castings Based on Non-Destructive Measurements During Fatigue Testing
Researchers developed a microstructure-specific lifetime prediction method for heavy-section ductile cast iron components using non-destructive measurements during fatigue testing, addressing the challenge that local microstructural variations in large castings significantly influence fatigue strength. The approach offers a more practical alternative to conventional specimen-based S-N curve determination for components such as wind turbine main shafts and planet carriers.
Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model
Not relevant to microplastics — this paper develops an optimized neural network model for predicting real-world load spectra and fatigue life of mechanical structures, achieving a fatigue life prediction accuracy of 93.56% for engineering applications.
Highly Sensitive Nonlinear Identification to Track Early Fatigue Signs in Flexible Structures
Researchers developed a physics-based and data-driven nonlinear system identification approach for detecting and tracking early fatigue damage in flexible aluminum structures subjected to vibration. The method estimates nonlinear parameters including geometric stiffness and cubic damping as a function of fatigue cycles, enabling real-time structural health monitoring.