Novel Calibration Strategy for Validation of Finite Element Thermal Analysis of Selective Laser Melting Process Using...

Area of Science:

• Materials Science and Engineering
• Additive Manufacturing
• Computational Modeling

Background:

• Selective laser melting (SLM) is an additive manufacturing process that relies on precise thermal control.
• Understanding the complex thermal history during SLM is crucial for predicting material properties and performance.
• Existing thermal analysis methods require accurate heat source models, which are challenging to calibrate.

Purpose of the Study:

• To propose and validate a novel calibration strategy for heat source models used in SLM thermal analysis.
• To enhance the accuracy of finite element method (FEM) simulations for the SLM process.
• To reduce the computational expense associated with optimizing heat source parameters.

Main Methods:

• In-situ temperature measurement using high-speed thermography for absorptivity calibration.
• Quantification of simulation error by comparing cross-sectional bead shapes from experiments and simulations.
• Application of Bayesian optimization to efficiently determine optimal heat source model parameters.

Main Results:

• The proposed calibration strategy achieved an error of less than 4% within 50 iterations.
• Bayesian optimization effectively identified optimal heat source parameters, minimizing simulation error.
• High-speed thermography provided accurate absorptivity calibration for the thermal model.

Conclusions:

• The novel calibration strategy significantly improves the accuracy of temperature field prediction in SLM.
• The integration of Bayesian optimization offers an efficient method for heat source model optimization.
• This approach enhances the reliability of thermal simulations in additive manufacturing.