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Multi-layer process control in selective laser melting: a reinforcement learning approach.

Stylianos Vagenas1, Taha Al-Saadi1, George Panoutsos1

  • 1Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, UK.

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|February 3, 2026
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Summary
This summary is machine-generated.

Reinforcement learning enhances control for powder bed fusion (PBF) 3D printing. This data-driven approach effectively manages complex thermal issues in selective laser melting, enabling better control over complete 3D parts.

Keywords:
Powder bed fusionProcess controlReinforcement learningSelective laser meltingTi–6Al–4V

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Area of Science:

  • Additive Manufacturing
  • Materials Science
  • Machine Learning

Background:

  • Powder bed fusion (PBF) is a key additive manufacturing technique for 3D parts.
  • Complex physical phenomena in PBF hinder analytical modeling and control-oriented 3D models.
  • Current PBF process control lags behind other manufacturing sectors due to these modeling challenges.

Purpose of the Study:

  • To introduce a reinforcement learning (RL) framework for advanced process control in PBF.
  • To address the lack of integrated, control-oriented models in PBF.
  • To demonstrate RL's efficacy for controlling multi-layer PBF processes, specifically selective laser melting.

Main Methods:

  • Utilized a reinforcement learning (RL) framework, a data-driven approach suitable for intricate or unknown process models.
  • Focused on selective laser melting, a laser-based PBF process.
  • Emphasized the importance of training stability in RL for PBF applications.

Main Results:

  • Successfully demonstrated the benefits of an RL process control framework for complete 3D parts (multiple layers).
  • Showcased effective management of heat accumulation issues inherent in PBF.
  • Confirmed the framework's ability to achieve robust overall process control.

Conclusions:

  • Reinforcement learning offers a viable solution for advanced process control in powder bed fusion.
  • The RL framework effectively addresses thermal management challenges, improving control over complex 3D part fabrication.
  • This research opens new avenues for improving PBF process stability and quality through intelligent control strategies.