## 1 Introduction  
The Fourth Industrial Revolution (4 IR) was coined by the World Economic Forum in 2016 in order to raise global income levels, thus improving life quality for the worldwide population [1]. In a strict sense, there are still remaining doubts about what exactly the 4 IR means in terms of scope and impact in technology development. The first industrial revolution (1784) yielded the use of steam to move mechanical equipment. Then, the second (1870) and third ones (1969) developed work division/mass production and automated production, respectively. Finally, the named 4 IR was originated based on internet employment and connectivity [2; 3]. However, COVID-19 opened a whole spectrum of opportunities ranging from remote medical consulting to digital manufacturing (DM). The global printed circuit board (PCB) manufacturing industry experienced a reduction in 2023 due the imbalances that resulted from the COVID-19 pandemic, and will currently grow by up to about 6.3%. In addition, a reported prediction suggests a market growth of about 12.5% for Flexi boards around 2031 [4]. Since China is the main PCB producer, due to the abundance of raw materials and advanced manufacturing technology, it points to opportunities to develop competitive technology for this market [5]. In that sense, the planning of this kind of project requires modern techniques that could help to evaluate them at early stages of new technology adaptation, or in terms of productivity. This suggests that the new paradigm, the DM, can be useful to help investors, researchers, and students to make better decisions; some examples are in references [6; 7; 8]. The DM involves several concepts and fancy automation techniques. One such technique is artificial intelligence (AI), which permits computers to simulate human behavior (intelligence and capacity) [9; 10]. An interesting branch of AI is computer vision (CV), because it is able to provide recommendations, which distinguishes it from image recognition tasks [9]. Thus, providing crucial information thorough detailed image analysis for the decision-making process within manufacturing operations is the cornerstone of the CV technique. In addition, the employment of sophisticated algorithms will enable the system to learn and optimize operations related to decision making. Image analysis through algorithms was employed by [PERSON] [11] to process images within a reconfigurable device. The above was attained by means of the Xilinx and Vivado tools, then the HW was designed through the \(C/C++\) language. However, due technical limitations in the development plate, it will be changed to obtain better results. In contrast, [PERSON] et al. [12] employed the Yolov8 model to detect 40 different classes of flying objects, and their results suggests that model mean average precision reached 99%. Thus, the authors obtained results within the state of the art. [PERSON] et al. [13] employed CV and an object detection system (YOLOv3-tiny) to detect, classify, and map coffee fruits during their harvesting. In this work, the authors recorded 90 videos at the discharge conveyor of arabica coffee (Catuai 144) on a Brazilian farm during 2020. The model peaked at the 3300 th iteration when an 800 \(\times\) 800 pixel image resolution was reached. Their results reached precisions of about 86%, 85%, and 80% for unripe, ripe, and overripe coffee fruits, respectively. Thus, this work could incentivize studies into the precision agriculture field and attributive mapping as well. A similar work was published by [PERSON] and [PERSON] [14], reaching efficiencies of around 95.2% after they analyzed 452 images on average per sample type. Defect detection through the Yolov8 algorithm and CV in manufacturing was employed by [PERSON] and co-workers [15]. The above was a complementary technique to automatic detection methods such as eddy current, infrared, and magnetic flux leakage, since such techniques have some environmental limitations.