相关实验视频
Updated: Jul 19, 2025

09:17
Surrogate Model Development for Digital Experiments in Welding
Published on: March 28, 2025
1.0K
使用机器学习方法对Cu-Al合金中的烧结密度进行建模
Saleh Asnaashari1, Mohammadhadi Shateri2, Abdolhossein Hemmati-Sarapardeh3
1School of Metallurgy and Materials Engineering, University College of Engineering, University of Tehran, Tehran 7761968875, Iran.
ACS omega
|August 14, 2023
概括
预测Cu-Al合金的烧结密度对于机械性能至关重要. 这项研究开发了先进的机器学习模型,MLP-LM显示出卓越的准确性,减少了昂贵的实验测量.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算建模 计算建模
背景情况:
- 铜 (Cu-Al) 合金中的烧结密度对于机械性能至关重要.
- 烧结密度的实验性确定是资源密集的.
- 需要准确的预测模型来优化粉末金工艺.
研究的目的:
- 开发和比较先进的机器学习模型,用于预测Cu-Al合金粉密度.
- 为了确定烧结密度的最准确的预测模型.
- 减少对广泛实验测试的需要.
主要方法:
- 采用了自适应增强决策树,支持向量回归,k-最近邻居,极端梯度增强和四个多层感知子 (MLP) 模型.
- 利用弹性反向传播,莱文伯格-马奎特 (LM),缩放的并联梯度和贝叶斯规范化来调整MLP.
- 输入参数包括输出强度,扬模量,相变体积变化,硬度,液态/固态相性质,烧结参数和粒子特性.
主要成果:
- 所有开发的模型在预测粉末密度方面都超过了现有的方法.
- 使用莱文伯格-马奎特 (MLP-LM) 优化的多层感知子模型显示了最高的精度.
- MLP-LM实现了1.292%的平均绝对百分比相对误差 (AAPRE) 和0.989.9的相关系数 (R).
- 使用杆技术检测异常值,确定了MLP-LM模型适用域之外的数据点.
结论:
- 先进的机器学习模型,特别是MLP-LM,为预测Cu-Al合金烧结密度提供了精确且有效的替代实验方法.
- 开发的模型可以显著帮助优化Cu-Al合金的粉末金工艺.
- 进一步的细化和验证,包括异常值分析,对于稳健的模型应用非常重要.
相关概念视频
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
81
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...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81
Modeling and Similitude
290
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
290
Metallic Solids
18.5K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
18.5K

