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Updated: Aug 10, 2025

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Process Parameter Selection for Production of Stainless Steel 316L Using Efficient Multi-Objective Bayesian

Timur Chepiga1, Petr Zhilyaev1, Alexander Ryabov1

  • 1Skolkovo Institute of Science and Technology, 121205 Moscow, Russia.

Materials (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

Optimizing additive manufacturing parameters using Bayesian optimization significantly reduces experiments. This method efficiently identifies laser powder bed fusion settings for desired part hardness and low porosity.

Keywords:
SS316Lmachine learningmetal additive manufacturingmulti-objective optimizationpowder bed fusionprinting parameters

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Computational Science

Background:

  • Additive manufacturing (AM) enables complex geometries but requires extensive parameter tuning.
  • Optimizing laser powder bed fusion (LPBF) parameters is crucial for part quality but is time-consuming and costly.

Purpose of the Study:

  • To investigate parameter optimization for the laser powder bed fusion (LPBF) process.
  • To identify optimal printing parameters for achieving desired part hardness and porosity.
  • To reduce the experimental effort required for process optimization.

Main Methods:

  • Utilized multi-objective Bayesian optimization with a Gaussian process surrogate model.
  • Trained the model on 57 empirical data points.
  • Employed efficient sampling to identify Pareto front solutions.

Main Results:

  • Achieved desired part hardness (224-235 HV) and low porosity (0.2-0.37%).
  • Identified optimal parameters: 58 W laser power, 257 mm/s scanning speed, 45 µm hatch distance, and 131° scan rotation.
  • Obtained three Pareto front solutions in just over six iterations.

Conclusions:

  • The proposed Bayesian optimization methodology significantly reduces experimental time and resources.
  • The validated parameters ensure high-quality parts with specific mechanical properties.
  • This approach offers an efficient solution for optimizing complex AM processes.