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Venturi Injector Optimization for Precise Powder Transport for Directed Energy Deposition Manufacturing Using the

Joshua García-Montagut¹, Rubén Paz¹, Mario Monzón¹

¹Department of Mechanical Engineering, University of Las Palmas de Gran Canaria (ULPGC), 35015 Las Palmas de Gran Canaria, Spain.

Materials (Basel, Switzerland)

|February 24, 2024

Summary

Optimized Venturi injectors precisely transport powder for additive manufacturing. Simulations improved powder suction by 85% in low-pressure systems, enhancing directed energy deposition processes.

Keywords:

additive manufacturing directed energy deposition discrete elements modeling genetic algorithms powder transport venturi

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Area of Science:

Materials Science
Mechanical Engineering
Additive Manufacturing

Background:

Directed energy deposition (DED) additive manufacturing relies on precise powder handling.
Venturi injectors offer potential for controlled particulate material transport.
Existing Venturi injector research often focuses on high-pressure systems and gravity feeding.

Purpose of the Study:

Optimize Venturi injector dimensional parameters for low-pressure powder transport.
Maximize powder suction and transport efficiency for additive manufacturing applications.
Achieve high precision in particulate material delivery.

Main Methods:

Utilized discrete element method (DEM) simulations for Venturi injector performance analysis.
Employed a genetic algorithm for design optimization based on initial experiments of design (DoE).
Performed statistical analysis to identify key design variables influencing powder transport.

Main Results:

Identified suction diameter (D3), throat diameter (d2), and nozzle diameter (d1) as critical parameters.
Determined optimal dimensional relationships: D3=34x particle diameter, d2=26.5x particle diameter, d1=40% of d2.
Achieved an 85% improvement in powder suction with a maximum 2% pressure loss.

Conclusions:

Optimized Venturi injector geometry significantly enhances powder transport efficiency.
The developed design is suitable for low-pressure additive manufacturing systems.
This optimization advances precise particulate material handling in DED processes.