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FSW Optimization: Prediction Using Polynomial Regression and Optimization with Hill-Climbing Method.

Piotr Myśliwiec¹, Paulina Szawara², Andrzej Kubit³

¹Department of Materials Forming and Processing, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, Poland.

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|January 25, 2025

Summary

This study optimized friction stir welding (FSW) using polynomial regression to predict maximum tensile load (MTL). Optimal parameters achieved a predicted MTL of 16,852 N, enhancing weld performance.

Keywords:

Design Expert 12 aluminum alloy 2024-T3 friction stir welding hill-climbing polynomial regression response surface analysis welding parameter optimization

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Area of Science:

Materials Science and Engineering
Manufacturing Processes
Computational Modeling

Background:

Friction stir welding (FSW) is a critical joining technique for various materials.
Optimizing FSW parameters is essential for maximizing the mechanical properties of welded joints, particularly the maximum tensile load (MTL).
Predictive modeling offers a pathway to efficient parameter optimization, reducing experimental costs and time.

Purpose of the Study:

To optimize the friction stir welding (FSW) process by developing a predictive model for maximum tensile load (MTL).
To identify optimal welding parameters (spindle speed and welding speed) for enhanced joint strength.
To validate the predictive model's accuracy and the optimization approach's effectiveness.

Main Methods:

Conducted 28 experimental runs varying spindle speeds (600-2200 rpm) and welding speeds (100-350 mm/min).
Developed a fifth-degree polynomial regression model to correlate welding parameters with MTL.
Employed Hill-climbing optimization and response surface analysis to determine optimal parameters and validate model predictions.

Main Results:

The fifth-degree polynomial regression model demonstrated a robust fit with high R-squared values.
Diagnostic tests indicated a non-normal distribution of MTL data, which was accounted for in the model.
Optimal parameters identified were 1100 rpm spindle speed and 332 mm/min welding speed, predicting an MTL of 16,852 N.

Conclusions:

Polynomial regression and Hill-climbing optimization are effective for predicting and enhancing the maximum tensile load in FSW.
The developed model provides strong predictive power for FSW joint strength.
The findings contribute to improving the performance and reliability of the friction stir welding process.