研究目的
To develop an SLM optimization system based on a supervised deep neural network for determining optimal process parameters to print Ti-6Al-4V products with desired density.
研究成果
The developed optimization system successfully determined optimal SLM process parameters for Ti-6Al-4V alloy, achieving a product with 99.8% relative density. This system reduces the need for extensive operator experience and experimentation time.
研究不足
The study is limited to Ti-6Al-4V alloy and does not explore other materials. The optimization system's performance is dependent on the quality and representativeness of the training data.
1：Experimental Design and Method Selection:
The study used a supervised deep neural network with Python and TensorFlow to optimize SLM process parameters.
2：Sample Selection and Data Sources:
Ti-6Al-4V ELI alloy powder was used, with process parameters including laser power, laser scanning speed, hatch distance, and layer thickness.
3：List of Experimental Equipment and Materials:
An SLM printer (MetalSys150) with an IPG ytterbium fiber laser was used.
4：Experimental Procedures and Operational Workflow:
The process involved printing models with a meander laser scanning strategy under argon gas to maintain low oxygen levels.
5：Data Analysis Methods:
The density of printed parts was measured using an analytical balance and density determination kit, with data analyzed using a deep neural network.
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Ytterbium fiber laser
YLR-200-AC-Y11
IPG Photonics
Used as the laser source in the SLM printer.
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SLM printer
MetalSys150
Winforsys Co., Ltd.
Used for selective laser melting of Ti-6Al-4V alloy.
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Analytical balance
GR-200
A&D Company, Ltd.
Used to measure the weight of the printed parts for density calculation.
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Density determination kit
AD-1653
A&D Company, Ltd.
Used in conjunction with the analytical balance to measure the density of the printed parts.
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