## 1 Introduction
Importantly, selected data regarding the criteria for evaluating assembly sequences can be obtained automatically from CAD assembly models, for example, the direction of joining parts obtained in this way is related to the number of changes in assembly direction for a specific sequence. Very important for this process are assembly features, which also have a direct impact on the assembly order of parts. Figure 1 presents a summary of the most commonly used criteria for optimizing the assembly process in the selection of assembly sequences in the published and analyzed scientific studies. The assembly sequence planning problem belongs to a general class of optimization problems known as NP-complete. For this kind of problem, it is necessary to query the whole set of permissible solutions to ensure that the optimal assembly sequence is found. Nevertheless, because this search strategy is very time consuming and impractical in many industrial applications which are complex, have multiple criteria, and often contain issues that prove difficult to optimize, other heuristic techniques are often applied to find a  
Figure 1: Criteria for the evaluation of assembly sequences in the analyzed scientific publications concerning ASP issues. solution close to the optimal one. One solution is also artificial neural networks, which is an information processing paradigm inspired by the natural biological nervous system. The very topic of assembly sequence planning using neural networks was covered in recent years by only a limited number of publications [8; 9]. The input vector in neural network is multiplied by the synaptic weights, which are the weight vector. This activity is related to the implementation of the function of the postsynaptic potential and the determination of the value of the y signal calculated based on the sum of input signals multiplied by synaptic weights. The models of artificial neurons can be perceived as mathematical models. What we consider to be the first model of a neural network is the neuron model proposed by [PERSON] and [PERSON] in 1943 and inspired by the biological model, following the pattern [9]:  
\[\text{Transfer function }\Rightarrow\ y=f(\sum_{i=1}^{k}x_{i}w_{i}+w_{0}) \tag{1}\]  
Usually, the signal path between neurons (processing units) is as shown in Figure 2, where \(x_{n}\) are the neuron input signals (or the external system input data), \(w_{n}\) are the weights of the edge-connections (synapses), \(w_{o}\) is the neuron's sensitivity threshold (i.e., bias), and \(f\left(\cdot\right)\) is a simple non-linear function, e.g., a sigmoid or logistic one. Activation (transfer) (AF) functions are possible for each of the hidden and output layers [3]. These studies are aimed at showing the possibility of predicting the assembly time of mechanical products based on variable factors influencing this parameter. The advantage of using artificial neural networks over other optimization algorithms is the ability to predict the assembly time without knowing the mathematical model that describes this phenomenon. This allows to obtain adequate results, also in the conditions of having incomplete production data. This procedure is indirectly aimed at indicating the assembly sequence by selecting the least time-absorbing solution. The article focuses on the application of artificial neural networks as a universal tool of artificial intelligence to support predictive tasks in the area of assembly of machine and device parts. The authors did not find any articles in which the issue of minimizing assembly time, which is important from the point of view of production efficiency, is solved with the use of art neural networks or other methods corresponding to the current trends in the use of artificial intelligence methods. Difficulties in developing an assembly time prediction procedure are mainly focused on providing an appropriate number of examples teaching the neural network. This was achieved by experimentally testing the operation of the network after each set of 100 examples was prepared.