Abagiu Marian Marcel, Cojocaru Dorian, Manta Florin, Mariniuc Alexandru
Faculty of Automation, Computers and Electronics, University of Craiova, 200585 Craiova, Romania.
Sensors (Basel). 2023 Jan 10;23(2):785. doi: 10.3390/s23020785.
This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. The solution was developed for an automotive manufacturer and the main goal of the implementation is the replacement of the visual inspection performed by a human operator with a computer vision application. We started by exploring different machine vision applications used in the manufacturing environment for several types of operations, and how machine learning is being used in robotic industrial applications. The solution implementation is re-using hardware that is already available at the manufacturing plant and decommissioned from another system. The re-used components are the cameras, the IO (Input/Output) Ethernet module, sensors, cables, and other accessories. The hardware will be used in the acquisition of the images, and for processing, a new system will be implemented with a human-machine interface, user controls, and communication with the main production line. Main results and conclusions highlight the efficiency of the CCD (charged-coupled device) sensors in the manufacturing environment and the robustness of the machine learning algorithms (convolutional neural networks) implemented in computer vision applications (thresholding and regions of interest).
本文描述了一种用于检测发动机缸体活塞腔内加工缺陷的解决方案的实施情况。该解决方案是为一家汽车制造商开发的，实施的主要目标是用计算机视觉应用取代人工视觉检查。我们首先探索了制造环境中用于几种操作类型的不同机器视觉应用，以及机器学习在机器人工业应用中的使用方式。该解决方案的实施重新利用了制造工厂中已有的且已从另一个系统退役的硬件。重新使用的组件包括相机、IO（输入/输出）以太网模块、传感器、电缆和其他配件。这些硬件将用于图像采集，并且为了进行处理，将实施一个新系统，该系统具有人机界面、用户控件以及与主生产线的通信。主要结果和结论突出了电荷耦合器件（CCD）传感器在制造环境中的效率以及计算机视觉应用（阈值处理和感兴趣区域）中实施的机器学习算法（卷积神经网络）的稳健性。
