*4.2. Discussion*

Presented method proposes the selection of the best assembly sequence based on the estimated assembly time for the selected product. It works on the basis of selected universal criteria for the evaluation of assembly sequences and their impact on the process time. In principle, its correct operation is based on constant production conditions, which is a prerequisite for its proper operation and correctness of the network learning process. Universal criteria for assessing the assembly sequence proposed in this paper can be effectively automatically retrieved from CAD documentation, although this is not the subject of the presented analysis. The obtained test results confirm that it is possible to develop procedures supporting the determination of the assembly sequence of mechanical products. The neural network model effectively predicts the time of the assembly process. Further research should focus on developing a more universal method and increasing the amount of data to enable network learning.

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. At the moment, the effectiveness of the network in the data verification group is 99%. Entering new data into the network will improve the efficiency of the time sensitive tasks and universally the possibility of applying the procedure to new, not considered cases.

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. A neural network model was developed to meet the requirements of all mechanical parts. The goal was to develop an overall model. Then, its effectiveness was verified on the basis of one selected product—the door of a forklift truck.
