| 基于CNN的FDM型3D打印故障诊断方法 |
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| 引用本文： | 卫诚琨,周俊,张嘉.基于CNN的FDM型3D打印故障诊断方法[J].农业装备与车辆工程,2022,60(2):149-153. |
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| 作者姓名： | 卫诚琨 周俊 张嘉 |
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| 作者单位： | 201620 上海市 上海工程技术大学 机械与汽车工程学院 |
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| 摘 要： | 热熔堆积(FDM)型3D打印时易受外部环境和参数设置的影响而发生故障,造成打印件的失败.为避免成型过程失败后继续打印,造成材料和时间的浪费,提出热熔堆积型3D打印故障自动诊断方法.该系统基于卷积神经网络(Convolutional Neural Network,CNN)的图像识别技术,通过监视、分析打印成型中零部件表面...
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| 关 键 词： | 热熔堆积 卷积神经网络 机器视觉 故障诊断 |
Fault Diagnosis of FDM 3D Printing Based on CNN |
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| Wei Chengkun,Zhou Jun,Zhang Jia.Fault Diagnosis of FDM 3D Printing Based on CNN[J].Agricultural Equipment & Vehicle Engineering,2022,60(2):149-153. |
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| Authors: | Wei Chengkun Zhou Jun Zhang Jia |
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| Institution: | (School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China) |
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| Abstract: | The Fused Deposition Modeling(FDM)3D printing is prone to failure due to the influence of external environment and parameter settings,resulting in the failure of the printed part.In order to avoid the waste of materials and time caused by printing after the failure of molding process,an automatic fault diagnosis method of hot-melt stacking 3D printing was proposed.The system is based on the image recognition technology of Convolutional Neural Network(CNN),which monitors and analyzes the image information of parts surface in the printing process to determine whether there is a fault in the printing process. |
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| Keywords: | Fused Deposition Modeling Convolutional Neural Network machine vision fault diagnosis |
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