**5. Conclusions**

The article describes a mechanical assembly time prediction system operating in a neural network, determined by the criteria: the number of tool changes, the number of assembly direction changes, or the stability of the assembly units. The principle of operation and training of the network is its work in a specific mechanical production period; it allows one to determine the most advantageous workplace configuration, production organization, process control, or level of employee training. It is necessary for the best possible network search results.

The obtained results of the analyses confirmed the effectiveness of the previously developed model. The authors assumed that it would also be suitable for other mechanical products, and further studies will be carried out to prove these assumptions. The development of a universal model for selecting the least time-consuming assembly sequence will make it possible to improve many assembly processes. This is of particular importance for products consisting of many parts and in complex manufacturing processes.

The obtained test results confirm that it is possible to develop procedures supporting the determination of the assembly sequence of mechanical products. The model of the neural network, containing universal criteria determining the time of the assembly process, was verified on the example of the assembly of the door of a forklift truck, confirming its effectiveness. Further research should focus on checking the usefulness of the neural network also for other mechanical products. The effectiveness of the method depends mainly on the number of cases teaching the neural network that are able to generalize the knowledge and the neural network to different products to be assembled. Thus, a network constraint may be a greater number of errors when predicting assembly time for other products. The aim of the authors is to develop the conducted research and verify the operation of the network on a wide range of products.

**Author Contributions:** Conceptualization, M.S.; methodology, K.P. and M.S.; software, K.P. and M.S.; validation, M.S. and K.P.; formal analysis, M.S. and K.P.; investigation, M.S. and K.P.; data curation, M.S.; writing—original draft preparation, M.S. and K.P.; writing—review and editing, K.P. and M.S.; visualization, K.P. and M.S.; supervision, M.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Ministry of Science and Higher Education of Poland (No. 0614/SBAD/1529).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
