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Mechanisms of Heat Transfer II

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Related Experiment Video

Updated: Jan 18, 2026

Surrogate Model Development for Digital Experiments in Welding

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Published on: March 28, 2025

Correlating Laser Additive Manufacturing Process with Heat Source Coefficients by Integrated Experimental and

Changrong Chen^1,2, Jingxin Zhou^1,2,3, Tianxin Zhao^1,2

¹Fujian Key Laboratory of Intelligent Machining Technology and Equipment, Fujian University of Technology, Fuzhou, China.

3D Printing and Additive Manufacturing

|September 11, 2025

Summary

This study introduces a novel response surface methodology (RSM) mapping approach for calibrating heat sources in laser additive manufacturing. The method accurately correlates process parameters with heat source coefficients, improving thermal modeling for better predictions.

Keywords:

finite element modeling heat source model inverse solution laser additive manufacturing response surface methodology

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Area of Science:

Materials Science
Mechanical Engineering
Computational Modeling

Background:

Accurate thermal field prediction is crucial for additive manufacturing (AM) process simulation.
Heat source representation significantly impacts the accuracy of numerical models predicting thermal and mechanical effects like distortions and residual stresses.

Purpose of the Study:

To develop and validate a method for calibrating heat source coefficients in AM simulations.
To establish a reliable correlation between AM process parameters and heat source characteristics using response surface methodology (RSM).

Main Methods:

Utilized response surface methodology (RSM) and analysis of variance (ANOVA) on central composite designs.
Employed nonlinear least squares method for inverse solving of heat source coefficients.
Correlated quantified influences of process parameters and heat source coefficients on bead geometry.

Main Results:

Established that both process parameters and heat source coefficients can be modeled using quadratic polynomials to predict bead geometry.
Demonstrated accurate modeling of connections between heat source coefficients and process variables using polynomial functions.
Quantified the effects of process factors and heat source model coefficients on experimental and numerical bead geometry.

Conclusions:

The proposed RSM mapping method is a feasible and reliable approach for heat process correlation in AM.
This research offers a convenient and accurate method for heat source calibration in laser additive manufacturing.
Improved thermal modeling through accurate heat source calibration leads to better prediction of AM process outcomes.