0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Human Health Effects Nanoplastics Sign in to save

Structure Refinement and Bauschinger Effect in fcc and hcp Metals

Metals 2023 3 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
V. V. Stolyarov

Summary

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.

Although the Bauschinger effect has been investigated in some detail in various materials, the number of articles on the effect of grain size is extremely limited, and in current nanostructured materials it is practically absent. Since such materials are considered as promising for structural applications, it is important to understand their mechanical behavior under conditions of changing the direction of deformation, and, therefore, it is necessary to study the Bauschinger effect and its dependence on grain size. The Bauschinger effect was investigated by a single exemplary method for tensile compression of commercially pure hcp titanium and fcc copper, with different grain sizes in the range from hundreds of microns to hundreds of nanometers. The change in grain size was performed by structure refinement by the method of severe plastic deformation using equal-channel angular pressing and subsequent annealing. It has been established that, in both materials, the Bauschinger effect increases with a decrease in grain size, the degree of permanent strain and the duration of exposure between forward and reverse deformation. The signs of the Bauschinger parameter in copper and titanium are opposite. The relationship between the Bauschinger effect and the nature of strain hardening in titanium and softening in copper in the ultrafine-grained state is discussed.

Sign in to start a discussion.

More Papers Like This

Article Tier 2

On Structural Sensitivity Of Young’s Modulus Of Ni-Rich Ti-Ni Alloy

This study explored how varying the initial microstructure and aging-induced precipitation in a Ni-rich Ti-50.8 at% Ni alloy modulates its Young's modulus over a wide range, providing guidance for tailoring implant materials to match the mechanical properties of bone.

Article Tier 2

On Structural Sensitivity of Young’s Modulus of Ni-Rich Ti-Ni Alloy

This study examined how grain size and heat treatment affect Young's modulus in nickel-rich titanium-nickel alloy for bone implant applications. This is a materials science paper focused on biomedical alloys with no direct relevance to microplastics or environmental health.

Article Tier 2

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.

Article Tier 2

Effect of Nb on the Damping Property and Pseudoelasticity of a Porous Ni-Ti Shape Memory Alloy

Researchers investigated the effects of adding varying amounts of niobium to porous Ni-Ti shape memory alloy on its damping properties and pseudoelasticity, finding that Nb addition reduced precipitates and was distributed as beta-Nb blocks in the matrix. Results showed that damping and pseudoelastic performance first increased then decreased with rising Nb content, pointing to an optimal composition for applications in aerospace and medical fields.

Article Tier 2

Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach

Researchers applied machine learning methods to predict the loading frequency of nickel-titanium shape memory alloys based on experimental data from cyclic tensile tests. They tested multiple algorithms across different loading frequencies and found that machine learning could effectively classify the frequency conditions. The study demonstrates the potential of data-driven approaches for characterizing the behavior of these materials used in engineering applications.

Share this paper