## 1 Introduction
### Related Works
#### 1.1.3 Process Control  
CNNs and RNNs are also studied by [18], with the goal of minimising the pre-processing of time series of data in complex system. The system is then validated with an experiment carried out on a service lift whose various types of anomalies are to be predicted. Machine learning tools are also applied for the purpose of monitoring the manufacturing process of robotic cells, which are automated in order to classify their output and possibly to set the parameters on-the-fly. A contribution with a system applied to laser additive manufacturing cells is made in [19]: the data are collected during the process from a low-cost coaxial camera and are processed by a CNN in order to detect the porosity of the artefact. Another example of process monitoring is that offered by [20]: they collect the process information of a hybrid laser-MAG (Metal Active Gas) welding system with two cameras at a high frame per second rate and extract the information through a convolution with the Gabor filter. A monitoring system for a robotic arc welding process is also proposed by [21]. Their solution is based on an optical system designed specifically for the application, whose images are processed by a CNN in order to classify the welding quality operated by the robot. Among the systems operating in real time, the insertion of an adaptive CNC machine parameter solution trained with neural networks to obtain the surface roughness within a desired value in an in-process manner is done by [22] Similarly, a controller applied to an injection moulded recycled plastic moulding system is implemented in [23]. Data collection is performed by an accelerometer, while data are processed using a classifier based on logistic regression. The approach described in [24] implements an integrated environment based on AR and force sensors. AR was tested through the use of two types of equipment: the first includes a fully immersive viewer, which reports the surrounding reality through the use of a camera, with information projected over the images reproduced by the viewer; the second, on the other hand, simply consists of a camera designed to monitor the work bench and a screen on which information is presented. The machine's understanding of the surrounding world is operated by tags positioned on the work bench. In [25], the authors developed a glove capable of monitoring the force and vibrations resulting from connecting two electrical terminals to understand whether the task was successful or not. AR technology has been also applied by [26]. In this article, AR was selected as a solution in order to facilitate new workers in carrying out activities requiring high skills, relying less on specialised training and, at the same time, reducing production scraps generated by human errors. As a result, 96% of users reported their interest in the tool. Returning to the simpler and less invasive Poka Yoke, we cite the proposal of [27], consisting of an analogical camera, an analogical-to-digital converter, and a computer based on an ARM processor. Their solution saves the image in binary format, to be then processed and classified by a Hopfield neural network (a declination of the RNN). The example studied by [28] also exploits computer vision. The authors' goal was to replace human operators during the visual inspection of product quality. The implemented system is based on a low-cost device (a Raspberry Pi) connected to a camera, which analyses the portion of the image in which the characteristic to be evaluated should be captured. The system was applied and tested in a processing cell with the aim of estimating in the aftermath the correct positioning of a component before being processed. Another simple and low-cost solution is the one designed in [29], the goal of which is to operate a fault detection and identification (FDI) in an O-ring positioning machine.