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机械合金化颗粒尺寸和形状的预测性随机建模

Predictive stochastic modeling of mechanically alloyed particle size and shape.

作者信息

Dwivedi Anand Prakash, Iranmanesh Emad, Sofokleous Katerina, Drakonakis Vassilis, Hamed Mashhadzadeh Amin, Dehaghani Maryam Zarghami, Golman Boris, Spitas Christos, Doumanidis Charalabos C

机构信息

Mechanical Engineering and Robotics, Guangdong Technion Israel Institute of Technology Shantou China

Department of Mechanical Engineering, Indian Institute of Technology Kanpur Kanpur India.

出版信息

RSC Adv. 2025 Jun 18;15(26):20682-20694. doi: 10.1039/d5ra03243a. eCollection 2025 Jun 16.

Abstract

Mechanical alloying of bimetallic materials by ball milling produces particulate products where, aside from internal structure, the size and shape of particles is of importance for various applications. This article introduces real-time modeling tools for the particle species demographics of size and aspect ratio, their dynamic evolution and dependence on processing conditions. Its highlight is a simple, analytical stochastic model of external particle features based on statistical formulations of impact energetics, friction and plastic deformation effects, as well as bonding and fracture transformations of the particles during the process. The model is calibrated and validated experimentally by measurements on laboratory micrographs and literature data in low- and high-energy ball milling of Al-Ni powders at different molar ratios. Its size and shape predictions offer insights to population growth of particles through mechanical alloying phenomena for material design and optimization and process observation for real-time feedback control.

摘要

通过球磨对双金属材料进行机械合金化会产生颗粒状产物,除内部结构外,颗粒的尺寸和形状对于各种应用也很重要。本文介绍了用于颗粒尺寸和纵横比的物种统计、其动态演变以及对加工条件依赖性的实时建模工具。其亮点是一个基于冲击能量学、摩擦和塑性变形效应的统计公式,以及过程中颗粒的结合和断裂转变的外部颗粒特征的简单解析随机模型。通过对不同摩尔比的Al-Ni粉末在低能和高能球磨中的实验室显微照片测量和文献数据,对该模型进行了实验校准和验证。其尺寸和形状预测为通过机械合金化现象实现颗粒群体生长提供了见解,有助于材料设计和优化以及用于实时反馈控制的过程观察。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2387/12175755/20fb3810ee4e/d5ra03243a-f2.jpg

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