相关实验视频

Updated: Jun 10, 2025

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

使用不同的机器学习模型预测NiTi高形状记忆合金中的格子参数

Tu-Ngoc Lam^1,2, Jiajun Jiang³, Min-Cheng Hsu¹

¹Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan.

Materials (Basel, Switzerland)

|October 16, 2024

概括

机器学习模型准确地预测形状记忆材料中的格子参数. 线性回归和随机森林模型对高温应用有希望.

科学领域:

材料科学材料科学材料科学
计算材料科学科学计算材料科学
机器学习应用机器学习应用

背景情况:

形状记忆材料 (SMM) 在高温应用中至关重要.
预测SMM中单临床B19'阶段的格子参数对于它们的性能至关重要.
现有的格子参数预测方法可能是计算密集的或不太准确.

研究的目的:

评估三种机器学习模型的有效性:线性回归 (LR),随机森林 (RF) 和支持向量回归 (SVR) 在预测格子参数方面.
将这些模型在两个不同的数据集中的预测精度进行比较:基于ZrO2的形状记忆陶 (SMC) 和基于NiTi的高形状记忆合金 (HESMA).
探索机器学习模型的综合方法,以提高预测准确度.

主要方法:

应用线性回归 (LR),随机森林 (RF) 和支持向量回归 (SVR) 模型.
培训和验证使用基于ZrO2的SMC和基于NiTi的HESMA的两个不同的数据集.
基于对格子参数 (a_c,a_m,b_m,c_m和β_m) 的预测准确度进行模型性能比较分析.

主要成果:

线性回归 (LR) 在基于NiTi的HESMA中显示出预测a_c,a_m,b_m和c_m的最高准确性.
随机森林 (RF) 在预测基于ZrO2的SMC和基于NiTi的HESMA的β_m方面表现出色.
支持向量回归 (SVR) 在两个数据集中显示出预测和实际格子参数值之间的最大偏差.

结论:

机器学习模型,特别是LR和RF,为预测形状记忆材料中的格子参数提供了一种可行且准确的方法.
结合RF和LR方法可以进一步提高对马氏体相的格子参数预测的准确性.
这些发现支持通过精确的计算预测开发先进的形状记忆材料,用于稳定的高温应用.

关键词:

线性回归是一种线性回归.机器学习是机器学习.随机的森林随机的森林形状记忆合金的合金支持向量的回归.

更多相关视频

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

相关概念视频

Trends in Lattice Energy: Ion Size and Charge

Trends in Lattice Energy: Ion Size and Charge

An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:

Temperature Dependent Deformation

Temperature Dependent Deformation

In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Hooke's Law

Hooke's Law

Hooke's law, a pivotal principle in material science, establishes that the strain a material undergoes is directly proportional to the applied stress, defined by a factor called the modulus of elasticity or Young's modulus.