Spatio-Temporal Adaptive Sampling for effective coverage measurement planning during quality inspection of free form surfaces using robotic 3D optical scanner
Spatio-Temporal Adaptive Sampling for effective coverage measurement planning during quality inspection of free form surfaces using robotic 3D optical scanner
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DOI：
10.1016/j.jmsy.2019.08.003
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发表时间：
2019-10-01
影响因子：
12.1
通讯作者：
Ceglarek, Dariusz
中科院分区：
文献类型：
--
作者：
Babu, Manoj;Franciosa, Pasquale;Ceglarek, Dariusz
使用机器人3D光学扫描仪对自由曲面进行在线尺寸检测提供了缩短产品质量缺陷检测平均时间的机会，因此已成为工业4.0实现近零缺陷的关键推动因素。然而，通过3D光学扫描仪检测大型工业尺寸金属板零件所需的时间经常超过生产周期时间（CT），因此限制了在线测量系统在高产量制造过程（例如在汽车工业中使用的那些）中的应用。本文通过开发时空自适应采样（STAS）方法来解决上述挑战，该方法具有以下能力：（i）基于自由曲面的部分测量来估计整个部件偏差;以及（ii）自适应选择下一个要测量的区域，以满足预定义的测量标准。这是通过以下方式实现的：首先，通过使用降维时空卡尔曼滤波器来对高维点云测量数据中的时空相关性进行建模;然后，在步骤S102，在检测过程中，通过结合部分测量数据来动态更新模型参数，以预测整个零件的偏差，并自适应地选择待测量零件的下一个关键区域。表面检测模型，主要是基于空间分析;空间-时间模型，它使用（i）空间分析来模拟零件变形;和，（ii）时间分析来模拟制造过程的自回归行为，用于预测下一个零件偏差。这提供了基于部分测量信息预测整个部件偏差的能力，从而减少了测量周期时间。使用机器人3D光学扫描仪测量汽车车门内部零件的工业案例研究演示了STAS方法，该方法导致（i）基于33%的零件表面的测量，整个零件偏差的预测误差在0.27 mm以内;以及，（ii）相应的CT减少42.2%，从当前最佳实践测量整个部件所需的510.5 s减少到部分测量部件所需的295.18 s。
In-line dimensional inspection of free form surfaces using robotic 3D-optical scanners provide an opportunity to reduce the mean-time-to-detection of product quality defects and has thus emerged as a critical enabler in Industry 4.0 to achieve near-zero defects. However, the time needed to inspect large industrial size sheet metal parts by 3D-optical scanners frequently exceeds the production cycle time (CT), consequently, limiting the application of in-line measurement systems for high production volume manufacturing processes such as those used in the automotive industry. This paper addresses the aforementioned challenge by developing the Spatio-Temporal Adaptive Sampling (STAS) methodology which has the capability for (i) estimation of whole part deviations based on partial measurement of a free form surface; and, (ii) adaptive selection of the next region to be measured in order to satisfy pre-defined measurement criterion. This is achieved by first, modelling spatio-temporal correlations in the high dimensional Cloud-of-Points measurement data by using a dimension reduced space-time Kalman filter; then, dynamically updating the model parameters during the inspection process by incorporating partial measurement data to predict entire part deviations and adaptively choose the next critical region of the part to be measured.The developed STAS methodology enhances the current free form surface inspection models, which are mostly based on spatial analysis; into spatio-temporal model, which uses (i) the spatial analysis to model part deformation; and, (ii) temporal analysis to model autoregressive behaviour of the manufacturing process for prediction of next part deviations. This provides capability to predict the whole part deviation based on partial measurement information and consequently reduces measurement cycle time. The industrial case study using a robotic 3D-optical scanner for the measurement of an automotive door inner part demonstrates the STAS methodology, which resulted in (i) a 3 Sigma error of prediction of whole part deviations within 0.27 mm based on measurement of 33% of the part surface; and, (ii) a corresponding CT reduction of 42.2% from 510.5 s required by current best practice to measure the whole part to 295.18 s required to partially measure the part.