Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control
Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control
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DOI：
10.1115/1.4042579
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发表时间：
2019-04-01
影响因子：
4
通讯作者：
Yang, Hui
中科院分区：
文献类型：
--
作者：
Imani, Farhad;Yao, Bing;Yang, Hui
现代制造业面临着根据个人需求定制产品的日益增长的需求，从而导致复杂设计的大量增加。为了应对设计复杂性，制造系统越来越多地配备先进的传感和成像能力。然而，传统的统计过程控制方法并不关注加工过程中的成像数据流。此外，对于从制造过程中收集的图像流中的非线性、不规则性和不均匀性的研究也很少。本文提出了联合多重分形和空隙度分析，以表征图像轮廓的不规则和不均匀模式，并检测制造过程中隐藏的动态。实验研究表明，所提出的方法不仅能有效表征超精密加工质量控制中的表面光洁度，还为将工艺参数与增材制造过程中获取的加工图像的分形特征联系起来提供了一个有效模型。这反过来将允许对工艺变化做出快速反应，从而减少次品数量。所提出的多重分形方法在超精密加工和增材制造等多个领域的过程监测和控制方面显示出强大的应用潜力。
The modern manufacturing industry faces increasing demands to customize products according to personal needs, thereby leading to the proliferation of complex designs. To cope with design complexity, manufacturing systems are increasingly equipped with advanced sensing and imaging capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in the image stream collected from manufacturing processes. This paper presents the joint multifractal and lacunarity analysis to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics in the manufacturing process. Experimental studies show that the proposed method not only effectively characterizes surface finishes for quality control of ultraprecision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed multifractal method shows strong potentials to be applied for process monitoring and control in a variety of domains such as ultraprecision machining and additive manufacturing.