**5. Conclusions**

**Figure 10.** Back-propagation neural network used to foresee the ultimate tensile strength.

218 Advanced Applications for Artificial Neural Networks

**Figure 11.** Predicted ultimate tensile strength by ANN versus the experimental data.

**Figure 12.** Regression line at the training stage.

The interest in ANN is growing so much that its models and algorithms are becoming standard tools in computer science and information engineering. This highlights the fact that after a long and productive youth, neural networks have formed a robust set of computation procedures with a robust theoretical base and undeniable effectiveness in solving real problems in different fields of information processing. In case study discussed in this work, the analysis performed has shown that the ANN simulation model can be used as a further effective method for predicting the FSW process. The MAPE obtained for the outputs micro hardness (HAZ) and ultimate tensile strength (UTS) were, respectively, 0.29% and 9.57%; R2 values were, in all cases, bigger than 0.90. Although the prediction of UTS was characterized by more high level of MAPE, if it is compared to HAZ estimated value, it was considered acceptable to ensure a model characterized by high reliability. The adoption of the simulation model can be very useful for the friction stir welding process. In fact, the use of tools for predicting the mechanical properties of the welds and for controlling the welding process, allows the production of welds with fewer defects. This reduces the number of repairs and costs, associated with the reiteration of the process. In the context of neural networks, the biggest question is "are we currently capable of building a human brain?" [85]. Undoubtedly, the achievement of these challenges is very ambitious, considering that the human brain has around 90 billion neurons shaping an extremely complex network, but in many cases the ANN can support the human behaviour, simplifying the decision making process, increasing the level cognition, under stress conditions, and increasing the capacity in evaluation and analysis of complex processes.
