Numerical Simulation Development and Computational Optimization for Directed Energy Deposition Additive Manufacturing...

Area of Science:

• Materials Science and Engineering
• Computational Mechanics
• Additive Manufacturing

Background:

• Additive Manufacturing (AM) offers cost-effective production with superior quality, driving demand for efficient simulation techniques.
• Accurate simulation of AM processes, particularly Directed Energy Deposition (DED), is crucial for improving product quality, lifecycle, and reducing production costs.
• Existing finite element simulations for large AM parts face challenges in computational time and data management due to numerous weld tracks.

Purpose of the Study:

• To develop a computationally efficient thermo-mechanical weld model for the DED process using 316L stainless steel.
• To accurately predict thermal behavior and residual stress in large AM parts at a reduced computational cost.
• To validate the developed model against experimental data for thermal and residual stress analyses.

Main Methods:

• Implemented single and multi-track thermal simulations for DED process adaptation.
• Applied a 'thermal cycle heat input' method by activating elements layer by layer cyclically to reduce computational time.
• Introduced a 'lumping of layers' strategy to further decrease computational demands, analyzing its impact on residual stress accuracy.

Main Results:

• Numerical simulations showed good agreement with experimental results for temperature trends during DED.
• The thermal cycle heat input method significantly reduced computational time.
• The lumping of layers strategy further decreased computational time, with validated accuracy for residual stress calculation using the contour cut method.

Conclusions:

• The developed thermo-mechanical weld model provides an efficient and accurate approach for simulating the DED process.
• The implemented strategies (thermal cycle heat input and layer lumping) effectively address computational challenges in simulating large AM parts.
• The model enables reliable prediction of thermal behavior and residual stress, crucial for optimizing AM part design and manufacturing.