![]() ![]() The survey’s findings show that in the context of Industry 4.0, low-cost equipment can be used to educate high-tech technologies. Based on a review of the available literature and current research projects, it can be concluded that using low-cost equipment and designing low-cost kits is a typical occurrence in academic and university practice. As a result, incorporating Industry 4.0 concepts into engineering curricula is one of the top priorities of academic institutions. ![]() Traditional education has contributed significantly to current levels of economic development and technological advancement. Universities emphasize their role as innovation testbeds and educators for future generations in the development of future technologies. ![]() The final test dataset contained 1300 images, of which 200 were in the desired state and the rest in the undesired state. We also augmented the test dataset, some elements of which can be seen in Figure 11, with various other objects that the convolutional neural network must correctly classify as undesired states during testing. The images of the undesired states, along with a portion of the images of the desired states, are subsequently only used when testing the trained CNN. With noise-added images, it is possible that we would introduce error into the resulting prediction during training. The same as can be seen in Figure 7, to create the training dataset that consisted of 1150 images. In this task, we can only use the images of the products in the desired state from the camera tunnel, supplemented with brightness-adjusted images. The process of preparing the dataset for training a convolutional neural network as well as designing the architecture of network itself is different from the previous task. However, due to a large number of students, economic factors (a significant number of resources, such as equipment and rooms, but also employees), or time constraints, it is not always possible to build as many practical sessions as the professors like. Traditional hands-on laboratories are critical in engineering for students to learn engineering practical skills. Many higher education institutions, faculties, and universities, have found a solution in the use of less expensive hardware, such as microcontroller platforms, sensors, and actuators, as well as software that can be used to program the hardware and is free for educational purposes, which can definitely help students in approaching programming industrial equipment and machines. It is critical to offer courses aimed on Industry 4.0 technologies at a reasonable cost, as this is critical for students in nations where Industry 4.0 is developing with a lower quality and for the country overall. Several studies say that the trend of digitalization in education is one of the most current solutions in the face of Industry 4.0. In recent years, digitalization has played an increasingly essential role in solving these challenges and expectations in students’ education. The results of the work serve as inspiration for educators and educational institutions. The solution is based on Arduino, TensorFlow and Keras, a smartphone camera, and is assembled using LEGO kit. Based on the overview of related works dealing with low-cost teaching solutions, we present in this paper our own low-cost Education Kit, for which the price can be as low as approximately EUR 108 per kit, for teaching the basic skills of deep learning in quality-control tasks in inspection lines. Therefore, the use of cheaper hardware and free software helps to create a reliable and suitable environment for the education of engineering experts. In recent years, there has been a great shortage of engineering experts, and therefore it is necessary to educate the next generation of experts, but the hardware and software tools needed for education are often expensive and access to them is sometimes difficult, but most importantly, they change and evolve rapidly. The main purposes of this paper are to offer a low-cost solution that can be used in engineering education and to address the challenges that Industry 4.0 brings with it.
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