Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel

J. Ciurana, G. Arias, T. Ozel

Research output: Contribution to journalArticle

132 Scopus citations


This article focuses on modeling and optimizing process parameters in pulsed laser micromachining. Use of continuous wave or pulsed lasers to perform micromachining of 3-D geometrical features on difficult-to-cut metals is a feasible option due the advantages offered such as tool-free and high precision material removal over conventional machining processes. Despite these advantages, pulsed laser micromachining is complex, highly dependent upon material absorption reflectivity, and ablation characteristics. Selection of process operational parameters is highly critical for successful laser micromachining. A set of designed experiments is carried out in a pulsed Nd:YAG laser system using AISI H13 hardened tool steel as work material. Several T-shaped deep features with straight and tapered walls have been machining as representative mold cavities on the hardened tool steel. The relation between process parameters and quality characteristics has been modeled with artificial neural networks (ANN). Predictions with ANNs have been compared with experimental work. Multiobjective particle swarm optimization (PSO) of process parameters for minimum surface roughness and minimum volume error is carried out. This result shows that proposed models and swarm optimization approach are suitable to identify optimum process settings.

Original languageEnglish (US)
Pages (from-to)358-368
Number of pages11
JournalMaterials and Manufacturing Processes
Issue number3
Publication statusPublished - Mar 1 2009


All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


  • Laser technology
  • Mold making
  • Neural network models
  • Surface roughness

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