![]() ![]() Especially, manual detection results by different observers are significantly different, resulting in the low measurement accuracy of the spray particle size. Manual methods used the uniformity of the liquid deposit in the spray chamber to detect spray particles, which only considered the particle density information. 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).Ĭonventional spray particle detection methods have disadvantages such as spray field interference, large subjective standard error, and an inability to specifically analyze the spray particle movement. 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. The re-used components are the cameras, the IO (Input/Output) Ethernet module, sensors, cables, and other accessories. The solution implementation is re-using hardware that is already available at the manufacturing plant and decommissioned from another system. 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 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. This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. ![]()
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