# Application of Neural Networks for Water Meter Body Assembly Process Optimization
## 1 Introduction  
Most artificial neural network models base their operation on highly simplified brain dynamics, although they increasingly reflect more real-life biological models. They are also increasingly used as computational tools to solve complex pattern recognition, function estimation and classification problems. Neutral networks can also be a solution to highly complex and multi-criteria problems related to various types of production processes, including assembly, machining and manufacturing processes. Examples of this type of solution are presented, inter alia, in [1], where the application of the neural network to additive manufacturing is presented. This paper overviews the progress of applying the neural networks algorithm to several aspects of the additive manufacturing, including model design, quality evaluation and current challenges, in applying NN in this field. The problem of applying NN in additive manufacturing. It has also been recently discussed in [2], where an example of the detection of processing defects in laser powder bed fusion additive manufacturing is presented. A convolutional neural network (CNN) was used in this case for autonomous detection and classification of many spreading anomalies. It is important that the input layer of the CNN is modified to enable the algorithm to learn both the appearance of the powder bed anomalies and contextual information at multiple size scales. This change increases the flexibility and classification accuracy of the algorithm. The use of neural networks in manufacturing process can also be found in many other works, such as in [3], where the problem of the optimization of milling operations using artificial neural networks (ANN) and simulated annealing algorithm (SAA) is discussed, or in [4], describing theoptimization of the manufacturing process parameters using deep neural networks as surrogate models. The considerations presented in this article concern an example of the application and selection of a neural network on the example of a specific production process. By building a model on a real working process, which will allow the network to be improved over time as the network is trained, an example of a highly useful working solution has been obtained, functioning in one of the largest water meters manufacturing companies in Europe. The article is, therefore, an example of applied science in industry, and its basis was the collection of hundreds of thousands of data, describing the process and parameters of products. The presented solution refers to the technological process of production, i.e., hot forging, machining and assembly of parts used to produce a device for measuring drinking water consumption. The quality requirements for this type of products put a strong emphasis on the final dimensional characteristics along with deviations in shape and position for the manufactured object after assembly operations. This has a critical impact on its further functioning in the operation/operational conditions. Low tolerance range for selected essential characteristics is a result of product legalization periods determined by the law and the strictly defined parameters of the accuracy of drinking water consumption measurements, which is important, among others, due to the care for the natural environment. One of those essential characteristics is the radial runout of the axle mounted in the hole in the bottom of the body. It is determined in relation to one of its internal diameters, which is influenced by the appropriate performance of not only assembly operations, but also technological operations preceding the assembly itself, which was the subject of this study. In the analyzed case, the field of radial runout tolerance (Figure 1) after assembly is 0.2 mm, which is a value that is difficult to achieve repeatedly in production. Construction of the assembly line and its elements is shown in detail in Figure 2.  
Figure 1: Determination of the radial runout after the assembly process.