Density prediction model of selective laser sintering parts

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作者：
机构：
[1] [1,Wang, Rong-Ji
[2] 1,Wang, Ling-Ling
[3] Zhao, Li-Hua
来源：
Wang, L.-L. (wanglling@sina.com) | 2005年 / Hunan University卷 / 32期
关键词：
Density (specific gravity) - Laser applications - Mathematical models - Neural networks - Processing - Rapid prototyping;
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摘要：
Selective laser sintering (SLS) processing parameters affect the density of the sintered parts, which affects the strength of the sintered parts. This paper presented a prediction model for the relationship between the processing parameters and the part density by using the neural network method. The effects of processing parameters, including the layer thickness, hatch spacing, laser power, scanning speed, temperature of work surroundings, interval time and scanning mode on part density were analyzed with the model. Experiment results showed that the neural network model might be used to analyze the relationship quantitatively. The model will allow users to produce parts with desired density by selecting appropriate parameter values prior to processing.
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