Predictive stochastic modeling of mechanically alloyed particle size and shape

Area of Science:

• Materials Science
• Mechanical Engineering
• Powder Metallurgy

Background:

• Mechanical alloying via ball milling is crucial for producing bimetallic powders with specific properties.
• Particle size and shape significantly influence the performance of these materials in various applications.
• Understanding particle evolution during milling is key for process optimization.

Purpose of the Study:

• To introduce real-time modeling tools for predicting particle size and aspect ratio demographics.
• To develop an analytical stochastic model for external particle features.
• To correlate model predictions with processing conditions and experimental data.

Main Methods:

• Development of a stochastic model based on impact energetics, friction, and plastic deformation.
• Incorporation of bonding and fracture transformations into the model.
• Experimental validation using micrographs and literature data from Al-Ni powder milling.

Main Results:

• The model accurately predicts particle size and shape evolution during mechanical alloying.
• Demonstrated dependence of particle demographics on processing conditions.
• Successful calibration and validation using low- and high-energy ball milling data.

Conclusions:

• The developed model provides insights into particle population dynamics during mechanical alloying.
• Enables material design and optimization by predicting particle characteristics.
• Facilitates real-time process observation and control for powder production.