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Enhanced Mathematical Model for Producing Highly Dense Metallic Components through Selective Laser Melting.

Jorge A Estrada-Díaz1, Alex Elías-Zúñiga1, Oscar Martínez-Romero1

  • 1School of Engineering and Science, Tecnologico de Monterrey, Av. E. Garza Sada 2501 Sur, Monterrey 64849, Mexico.

Materials (Basel, Switzerland)
|April 3, 2021
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Summary

This study enhances a mathematical model to predict the bulk density of Selective Laser Melting (SLM) components. The improved model accurately predicts density using key processing parameters for various metal alloys.

Keywords:
densificationmanufacturing parametersmathematical modelingselective laser melting

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Additive Manufacturing

Background:

  • Predicting the bulk density of components produced via Selective Laser Melting (SLM) is crucial for ensuring their mechanical integrity and performance.
  • Existing mathematical models often lack the comprehensive inclusion of key processing parameters that influence SLM part density.
  • Understanding the relationship between processing parameters and material properties is essential for optimizing SLM processes.

Purpose of the Study:

  • To enhance a previously developed mathematical model for predicting the bulk density of SLM components.
  • To incorporate laser power, scanning speed, hatch spacing, powder thermal conductivity, and specific heat capacity as independent variables.
  • To develop an analytical expression for predicting bulk density and derive a method for determining optimal scanning speed for high density.

Main Methods:

  • The study enhances a pre-existing mathematical model by incorporating five independent variables: laser power, scanning speed, hatch spacing, powder thermal conductivity, and specific heat capacity.
  • Literature data on the SLM of various metallic materials (aluminum, steel, titanium, copper, tungsten, and nickel alloys) were utilized to validate the enhanced model.
  • Statistical analysis, including the calculation of root-mean-square-error (RMSE), was performed to assess the model's accuracy.

Main Results:

  • A strong correlation was observed between dependent and independent dimensionless products across all studied metallic materials.
  • The enhanced mathematical model demonstrated high accuracy, with computed RMSE values not exceeding 5 × 10-7.
  • An analytical expression for predicting the bulk density of SLM components was successfully developed.

Conclusions:

  • The enhanced mathematical model provides a highly accurate method for predicting the bulk density of SLM components.
  • The derived analytical expressions enable the prediction of bulk density and the determination of optimal processing parameters, specifically scanning speed relative to laser power, for achieving high-density SLM parts.
  • This work contributes to the optimization and quality control of additive manufacturing processes for metallic materials.