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Optimization of Selective Laser Melting Parameter for Invar Material by Using JAYA Algorithm: Comparison with TLBO,

Hiren Gajera1, Faramarz Djavanroodi2,3, Soni Kumari4

  • 1Department of Mechanical Engineering, L D College of Engineering, Ahmedabad 380015, India.

Materials (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

Optimizing selective laser melting of Invar-36 requires specific parameters. A 90° orientation, 136 KW laser power, and 650 mm/s scanning speed yield optimal hardness and surface roughness for aerospace applications.

Keywords:
ANOVADMLSGAJAYATLBOhardnessinvarsinteringsurface roughnesstaguchi

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Area of Science:

  • Materials Science and Engineering
  • Additive Manufacturing
  • Surface Engineering

Background:

  • Invar-36 is critical in aerospace and thermostat manufacturing.
  • Mechanical and metallurgical properties determine final product quality.
  • Selective laser melting (SLM) is a key additive manufacturing process.

Purpose of the Study:

  • To evaluate the influence of laser power, scanning speed, and orientation on Invar-36 hardness and surface roughness in SLM.
  • To identify optimal process parameters for desired material properties.
  • To compare the JAYA algorithm with TLBO and GA for optimization effectiveness.

Main Methods:

  • Analysis of Variance (ANOVA) to test model significance.
  • Fuzzy-based JAYA algorithm for parameter optimization.
  • Comparative analysis of JAYA, TLBO, and Genetic Algorithm (GA) optimization outcomes.

Main Results:

  • Identified optimal parameters: 90° orientation, 136 KW laser power, and 650 mm/s scanning speed.
  • These parameters significantly improve hardness and surface roughness of Invar-36 parts.
  • The JAYA algorithm demonstrated effectiveness in achieving optimal process parameters.

Conclusions:

  • The study successfully determined the optimal SLM parameters for Invar-36.
  • Achieving desired hardness and roughness is feasible with precise parameter control.
  • The JAYA algorithm provides a robust method for optimizing additive manufacturing processes.