# Application of Neural Networks for Water Meter Body Assembly Process Optimization
## 2 Materials and Methods  
### Artificial Neural Networks in Mechanical Assembly Problem  
Due to the fact that designing and optimizing the technological process of assembly is a very complex, multi-criteria issue [5; 6; 7], various heuristic techniques are used for these problems, resulting in finding approximately optimal solutions, due to specific criteria. This problem in most cases concerns ASP (assembly sequence planning) problem and can be solved, for example, by selected heuristics. In research on ASP, very different heuristic optimization algorithms were used, such as the genetic algorithm (GA) [8; 9; 10] or artificial neural networks (ANN), that are the main subjects of this article [11; 12; 13]. In [14] the authors came up with a three-step integrated solution with some heuristic working rules to help generate an efficient sequence of assembly. In the mentioned paper, the use of a backpropagated neural network for optimization of the assembly sequence is presented. The outcome showed that the introduced model can enable the optimization of the assembly sequence and allows the designer to perceive the contact relationship and assembly limitation of the three-dimensional elements. Attempts to use ANN in mechanical assembly can also be found in other applications. The paper [15] presents the application of the method of artificial neural networks to detect  
Figure 2: Assembly line for water meter bodies with elements: 1—Storage bay with a basket of processed water meter bodies intended for assembly components, 2—Auxiliary tray for machined bodies, 3—Station for assembling the strainer and damming plate in the water meter body, 4—Transitional storage bay for partially assembled bodies, 5—Station for mounting the axle in the water meter body, 6—Control of the depth of the axle seating in the water meter body, 7—Storage bay with a basket for the assembled water meter bodies. faulty assembly processes with the use of wearable technology. The paper deals with the problem of distinguishing between correct and faulty operations in assembly tasks. A digital assembly glove was used for this purpose. To classify the signal from the glove, artificial neural network methodology was used. The network performance was tested in the selected assembly process. In the paper [16], authors presented system for assembly parts recognition, applying convolutional neural networks. Deep neural networks have been used to identify and recognize mounting parts (bolts, nuts) and mounting features (holes) to speed up any augmented reality mounting process. The article [17] presents the use of component models instead of the entire assembly model for predicting assembly quality defects during the production of automotive equipment. For this purpose, artificial neural networks were used to predict assembly time and market value based on assembly models. Artificial neural networks and also machine learning [18; 19; 20; 21; 22] are, thus, information processing paradigms inspired by the natural biological nervous system. The decision network consists of a large number of highly interconnected processing elements (neurons) that perform some basic operations on their input to solve a specific problem. The neurons that make up the network are linked together by synapses. Synapses have assigned weights, i.e., numerical values, the interpretation of which depends on the model (in the studied case the model of technological assembly process). The input vector is multiplied by the synaptic weights vector. This operation performs a function of the postsynaptic potential and allows one to determine the value of the signal calculated on the basis of the sum of input signals multiplied by synaptic weights. Artificial neuron models can be interpreted as mathematical models.