# Computational Framework to Model the Selective Laser Sintering Process

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## Abstract

**:**

## 1. Introduction

#### 1.1. Selective Laser Sintering

#### 1.2. State of the Art

#### 1.3. Objectives and Work Outline

## 2. Computational Framework

#### 2.1. Power Bed Formation

^{3}. This value is very close to the experimental value of 0.45 /cm

^{3}[37], thus demonstrating the suitability of the simulation for analyzing the SLS process at the desired particle length scale, despite the simplifications made. The powder bed formation simulation consists of a box and a moving blade, both illustrated in Figure 2a. Initially, the particles, with a realistic particle size distribution, are inserted into the domain (Figure 2b), and then the blade moves, dragging them along (Figure 2c), until they fall inside the desired box and the excess particles are deleted (Figure 2d). That box limits the representative section which spans, vertically, approximately two material layers.

#### 2.2. Sintering Simulation

#### 2.3. Solver Assessments

## 3. Solver Improvement

#### 3.1. Current Limitations

#### 3.2. Modified Solver

## 4. Case Studies

#### 4.1. Hatch Distance

#### 4.2. Laser Power

#### 4.3. Energy Density Studies

#### 4.4. Coalescence Development

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AM | Additive Manufacturing |

SLS | Selective Laser Sintering |

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**Figure 1.**General representation of the selective laser sintering (SLS) process (adapted from [4]).

**Figure 2.**Powder bed formation simulation. (

**a**) Geometry dimensions; (

**b**) particle insertion; (

**c**) blade movement; (

**d**) final state (adapted from [4]).

**Figure 4.**Comparison between the coalescence behavior before (

**left**image) and after (

**right**image) the code adaptation, demonstrating the multiple viscosity implementation.

**Figure 5.**Schematic representation of the implemented code approach to surpass the identified limitation.

**Figure 6.**Hatch distance studies for the highest (

**left**image) and lowest (

**middle**image) values, with the temperature profiles for the latter (

**right**image).

**Figure 8.**Energy density studies for the same energy density, altering only the laser power (

**left**image) or scan speed (

**right**image).

**Figure 9.**Coalescence development studies showing the sintering evolution after five seconds and highlighting defects related to it.

Parameter | Value | Units |
---|---|---|

Laser Power | 17.1 | W |

Scan Speed | 3000 | mm/s |

Hatch Distance | 0.3 | mm |

Density | 1000 | kg/m^{3} |

Thermal Conductivity | 0.2 | W/(m K) |

Viscosity (at 474 k) | 390/5095 | Pa·s |

Surface Tension | 0.035 | N/m |

Absorption Coefficient | 1.3 × 10^{4} | m^{−1} |

Powder Refresh Rate | 50 | % |

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**MDPI and ACS Style**

Castro, J.; Nóbrega, J.M.; Costa, R.
Computational Framework to Model the Selective Laser Sintering Process. *Materials* **2024**, *17*, 1845.
https://doi.org/10.3390/ma17081845

**AMA Style**

Castro J, Nóbrega JM, Costa R.
Computational Framework to Model the Selective Laser Sintering Process. *Materials*. 2024; 17(8):1845.
https://doi.org/10.3390/ma17081845

**Chicago/Turabian Style**

Castro, João, João Miguel Nóbrega, and Ricardo Costa.
2024. "Computational Framework to Model the Selective Laser Sintering Process" *Materials* 17, no. 8: 1845.
https://doi.org/10.3390/ma17081845