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Simulation of 316L Stainless Steel Produced the Laser Powder Bed Fusion Process.

Ľuboš Kaščák¹, Ján Varga¹, Jana Bidulská²

¹Department of Technology, Materials and Computer-Aided Production, Faculty of Mechanical Engineering, Technical University of Košice, Letná 9, 04002 Košice, Slovakia.

Materials (Basel, Switzerland)

|December 23, 2023

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Weight Factor as a Parameter for Optimal Part Orientation in the L-PBF Printing Process Using Numerical Simulation.

Materials (Basel, Switzerland)·2024

Simulation tools predict errors in additive manufacturing. For 316L stainless steel using laser powder bed fusion (L-PBF), simulations achieved high accuracy in predicting and compensating for part distortions.

Area of Science:

Materials Science
Manufacturing Engineering
Computational Engineering

Background:

Additive manufacturing (AM) is vital for producing complex industrial parts.
Simulation tools are essential for error prevention and understanding material behavior during AM.
316L stainless steel is a common material in AM applications.

Purpose of the Study:

To simulate the laser powder bed fusion (L-PBF) process for 316L stainless steel.
To predict potential errors and deformations during the AM process.
To evaluate the effectiveness of simulation in minimizing part distortion.

Main Methods:

Utilizing the Simufact Additive software for process simulation.
Simulating the L-PBF process for 316L stainless steel test samples.

Keywords:

Simufact Additive cryogenic conditions mechanical properties metal additive manufacturing

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Analyzing predicted deformations and implementing distortion compensation.

Main Results:

Simulation successfully predicted deformations and potential errors in the L-PBF process.
Achieved high accuracy in predicting maximum deviation of -0.01 mm.
Achieved high accuracy in predicting minimum deviation of -0.13 mm after distortion compensation.

Conclusions:

Simulation tools like Simufact Additive are effective for predicting and mitigating errors in metal additive manufacturing.
The L-PBF process for 316L stainless steel can be optimized through simulation-driven distortion compensation.
Accurate prediction of part deviations is crucial for successful industrial application of AM.