A layer-by-layer quality monitoring framework for 3D printing
A layer-by-layer quality monitoring framework for 3D printing
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DOI：
10.1016/j.cie.2021.107314
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发表时间：
2021-04-29
影响因子：
7.9
通讯作者：
Lei, Shuting
中科院分区：
文献类型：
--
作者：
Bisheh, Mohammad Najjartabar;Chang, Shing, I;Lei, Shuting
增材制造技术的发展正在加速从大规模生产向大规模定制的转变。在这一转变中，包括质量控制在内的所有生产阶段的自动化是关键。在这项研究中，提出了一种分层框架来基于俯视图像监控 3D 打印零件的质量。所提出的统计过程监控方法从自启动控制图开始，仅需要两次成功的初始打印。为了应对照明带来的图像处理挑战，采用机器学习 (ML) 方法将每一层与打印床分开。将样本图像与每一层的良好部分的标准图像进行比较。差异图像中的像素数量被输入到建议的控制图中，以监控每一层的打印过程。基于像素数量的指数加权移动平均（EWMA）图表用于每层的过程监控。一旦打印了足够的零件，同质层就会聚集在一起，以减少过程监控所需的控制图的数量。基于3英寸直径篮子部件的实验结果表明，所提出的基于逐层图像连续监控的框架能够检测打印过程中的微小变化。
Technology development in additive manufacturing is accelerating transition from mass production to mass customization. In this transition, automation in all stages of production including quality control is a key. In this study, a layer-wise framework is proposed to monitor quality of 3D printing parts based on top-view images. The proposed statistical process monitoring method starts with self-start control charts that require only two successful initial prints. Answering the challenges of image processing due to lighting, a Machine Learning (ML) method is adopted to separate each layer from the printing bed. A sample image is compared to the standard image from a good part at each layer. The number of pixels in the difference images is fed into the proposed control charts to monitor printing process at each layer. An Exponentially Weighted Moving Average (EWMA) chart based on the number of pixels is used for process monitoring at each layer. Once enough parts have been printed, homogeneous layers are clustered to reduce the number of control charts needed for process monitoring. Experimental results based on a 3-inch diameter basket part show that the proposed framework based on continuously monitoring of layer-by-layer images is able of detecting small changes in printing process.