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Prediction of Surface Roughness in Gas-Solid Two-Phase Abrasive Flow Machining Based on Multivariate Linear Equation.
Wenhua Wang1, Wei Yuan1,2, Jie Yu1
1School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.
Micromachines
|October 27, 2022
Summary
This study developed a surface roughness prediction model for Gas-Solid Two-Phase Abrasive Flow Machining. The model accurately predicts surface roughness with minimal error, optimizing the machining process.
Area of Science:
- Manufacturing Engineering
- Materials Science
Background:
- Surface roughness is a critical parameter in manufacturing processes.
- Gas-Solid Two-Phase Abrasive Flow Machining (GS-TPAFM) is an advanced material removal technique.
Purpose of the Study:
- To develop and validate a surface roughness prediction model for GS-TPAFM.
- To identify optimal processing parameters for achieving desired surface finish.
Main Methods:
- Orthogonal experimental design was employed.
- Q235 steel and white corundum abrasives of varying particle sizes were used.
- Range method and factor trend graphs analyzed experimental data.
Main Results:
- An optimal parameter combination (A3B2C1D2) was determined.
- A multiple linear regression equation was established for surface roughness prediction.
- The developed model demonstrated high reliability with a maximum error of 0.339 μm.
Conclusions:
- The developed prediction model effectively forecasts surface roughness in GS-TPAFM.
- The study provides a reliable method for optimizing machining parameters for surface quality.
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