研究目的
To analyze and optimize the influence of laser processing parameters in inducing residual compressive stress with minimal surface deformation on Ti6Al4V alloy using finite element simulation and Taguchi Grey Relational Analysis.
研究成果
The study successfully develops a finite element model for LSP simulation on Ti6Al4V alloy, validated by correlation with experimental results. Taguchi Grey Relational Analysis identifies optimal process parameters for inducing compressive residual stresses with minimal surface deformation. The number of laser shots is found to be the most influential parameter, followed by laser peak pressure, spot diameter, pressure pulse duration, and spot overlap.
研究不足
The study is limited to the simulation of LSP on Ti6Al4V alloy and does not account for all possible real-world variations in material properties and laser parameters. The validation is based on correlation with published results, which may not cover all aspects of the LSP process.
1:Experimental Design and Method Selection:
The study employs a finite element numerical simulation model for Laser Shock Peening (LSP) on Ti6Al4V alloy, utilizing Johnson-Cook’s visco-elastic-plastic material behavior law and Gaussian pressure profile for uniform loading. Taguchi Grey Relational Analysis (TGRA) with L27 orthogonal array is applied for optimization.
2:Sample Selection and Data Sources:
A Ti6Al4V material model is used for LSP simulation. Data for validation is sourced from published experimental results.
3:List of Experimental Equipment and Materials:
ABAQUS/Explicit software is used for simulation. The material properties include Young’s modulus, Poisson’s ratio, and density for Ti6Al4V, along with Johnson-Cook material plastic properties.
4:Experimental Procedures and Operational Workflow:
The simulation involves creating a 3D model of the Ti6Al4V alloy, meshing it, applying Gaussian pressure pulse as loading, and analyzing the residual stresses and surface deformation. The process parameters include laser spot diameter, overlap percentage, peak pressure, number of shots, and pressure pulse duration.
5:Data Analysis Methods:
The results are analyzed using Taguchi Grey Relational Analysis for multiple response optimization. ANOVA is used to evaluate the influence of process parameters on response variables.
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