## 1 Introduction
Their results depict a generalized enhancement in the operation, since the precision increases from 98.1% to 99.3%. Another work related to CV for manufacturing processes is presented in reference [16], where promising results open a wide range of possibilities for DM. [PERSON] and co-workers [17] implemented a CV-based robotic arm for object color, shape, and size detection, by using the image analysis procedure with the PixyCMU camera sensor to distinguish multiple objects according to their colors, which, in their work, were red, yellow, and green. After 10 repetitions, an average accuracy of 80% was obtained, with the red-colored one having the highest accuracy (90%) when compared to the other two. However, the accuracy of the color detection was influenced by changes in lighting conditions and the object distance. Finally, the authors reported an accuracy of 100% for shape detection characteristics (circle, triangle, and rectangle, to mention few). [PERSON] et al. [18], published a work related to stereo vision-based object recognition by means of convolutional neural network (R-CNN). Their results suggest that R-CNN was able to recognize triangle blocks with 100% for accuracy, recall, and precision. On the other hand, a precision of about 96.88% in identifying square blocks was obtained, while 100% and 98.44% for recall and accuracy were obtained, respectively. Similarly, [PERSON] [19] reported the application of a CNN for stereo vision. In that sense, the authors proposed the new ModuleNet model for stereoscopic vision, showing that, both qualitatively and quantitatively, they performed the Census-Hamming approach. The manufacturing processes yield challenges when the operations do not obtain the planned results due to unforeseen factors. In that sense, [PERSON] et al. [20] proposed a digital twin (DT)-based cyber-physical system for automotive body production lines. The DT developed was designed to predict if a certain model of automotive body can be manufactured within a required schedule for a customer by considering abnormal scenario occurrence. The results suggest that an average prediction performance of 96.83% can be obtained for the actual production plan that includes the product, process, plan, plant, and resource information model. Previously, [PERSON] [21] proposed a DT for the supply chain in different scenarios. As mentioned before, the 4 IR relies on DT for the development of the DM, which can help to prevent unforeseen events. Also, DT can be usefully employed to scale plan manufacturing activities that can be extrapolated to a productive process. In that sense, the aim of this work is to present a scaled manufacturing system for PCBs that includes CV technology to process images, thus achieving process segmentation in images of a PBC scaled manufacturing system. The plan for the above is to build a DM scaled system that can be used to predict, plan, and actualize a complete manufacturing plant for the components mentioned before. Different types of manufacturing cells, especially those involving human interaction for tasks such as adding or removing supplies, adjusting elements, or simply interacting with the cell, can experience changes in element positions, omissions of necessary components, or the introduction of foreign objects. These variations can adversely affect the proper functioning of the cell, particularly the robotic arms responsible for transporting boards between machines, inspection points, and cleaning stations in the case study. Implementing an image processing system that identifies elements within the manufacturing cell provides advance information on the presence and position of essential components for proper operation. This enables the adjustment of processes executed by the manufacturing cell, particularly the actions of the robotic arms. For example, if an element required for a preprogrammed action is missing, the system can omit that action and perform only those tasks that are feasible based on the detected elements.