Faculty of Mechanical Engineering, Slovak University of Technology in Bratislava, Námestie Slobody 17, 812 31 Bratislava, Slovakia.
SOVA Digital a.s. Bojnická 3, 831 04 Bratislava, Slovakia.
Sensors (Basel). 2021 Mar 5;21(5):1797. doi: 10.3390/s21051797.
The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.
本文探讨了基于颜色识别运动中工业产品的各个方面。为此目的，创建了一个带有传送带、机器人和工业颜色传感器的自动化机器人工作场所。测量数据在数据库中进行处理，然后以 A 类标准不确定度和 B 类标准不确定度的形式进行统计评估，以获得综合标准不确定度结果。根据获得的数据，为识别产品创建 RGB 颜色分量的控制图。监测产品速度对测量过程识别和过程稳定性的影响。在识别不确定性的情况下，即测量值超出控制图的限制时，使用 K-最近邻机器学习算法。该算法基于到分类值的欧几里得距离来估计其最准确的迭代。这导致了在传送带上移动的产品的综合识别系统，其中基于数据收集和使用机器学习的统计分析，展示了工业使用的可靠性。
