Fast Detection of Heat Accumulation in Powder Bed Fusion Using Computationally Efficient Thermal Models.

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Science

Background:

• Powder Bed Fusion (PBF) is an Additive Manufacturing (AM) technique offering high design freedom for complex geometries.
• PBF processes are susceptible to defects like melt ball and dross due to local heat accumulation, impacting part quality and microstructure.
• Conventional layer-by-layer thermal models for PBF are computationally expensive, limiting their application to part-scale analyses.

Purpose of the Study:

• To develop computationally efficient thermal models for predicting local heat accumulation in PBF parts.
• To analyze the trade-offs between simplification and accuracy in thermal process modeling for PBF.
• To enable faster design evaluations and integration with optimization algorithms for PBF components.

Main Methods:

• Utilized a well-established layer-by-layer PBF thermal process model as a baseline.
• Introduced three novel physics-based simplifications derived from the analytical solution of the one-dimensional heat equation.
• Evaluated the computational cost and accuracy of simplified models in predicting heat accumulation zones.

Main Results:

• The proposed simplifications significantly reduce computational burden while maintaining accuracy in predicting heat accumulation.
• The steady-state thermal response simplification achieved a 600x speedup compared to conventional analysis.
• Simplified models effectively detect problematic part features related to overheating.

Conclusions:

• Novel simplified thermal models offer substantial computational benefits for PBF process simulation.
• These models facilitate rapid detection of potential defects and enable quicker design iterations.
• The developed approach paves the way for integrating efficient thermal analysis into PBF design optimization workflows.