## 5 Algorithm Application and Results  
### Evaluation of Components and Indicators  
To use artificial neural networks, it is first necessary to describe the desired outcome of the model and, from there, determine how the task should be handled. Adhesive materials intrinsically have many variables that determine their overall bonding strength [41; 42]. For a given adhesive and joint, damage can limit the joint strength to a specific interval, between no adhesion up to full-strength adhesion. Therefore, the problem at hand is considered as involving classification into a given number of weak adhesion classes, rather than a regression problem. A total of 20 classes were chosen, as this was a large enough  
Figure 7: Representation of how the forces were simulated by discretizing and applying them to the edges of a square that has the same dimensions as the actuator. value to visualize any possible damage progression, while still small enough to allow for distinct classes. With the basic concept chosen, various parameters can immediately be defined. Thus, the evaluation metric or loss function adopted was the categorical cross-entropy, and all final layers before the output contained the softmax activation function with a kernel equal to 20, that is the number of classes. Finally, the accuracy metric was chosen, as it is widely implemented in classification problems and is a simple metric to assess the different models that were used. To train the model, the data were divided into training and testing in the proportion 70% and 30%, respectively, as these percentages are classic values that work in most cases and give a good baseline to initiate the study. Finally, the chosen epoch size was 400, as the objective of this work was to properly understand each algorithm and not allow for an early stop, which could exclude important behaviors. This may cause overfitting of the training data, but this was not considered a concern during the analysis, as it was an expected outcome. Although there would be a constant of 100 epochs, each algorithm was also evaluated accordingly with the epoch at which the accuracy started to converge and how long this process took. To give the best evaluation of each model for each type of dataset, it is necessary to assess several parameters and algorithm architectures. These parameters vary from algorithm to algorithm, and their optimization requires an extensive grid search to determine, inside the chosen grid, the best parameters. To minimize the total time expended in such an optimization, a broad trend search was conducted, where the total accuracy was compared to the time spent to reach the solution. This allowed the algorithm not only be efficient, but also faster when compared to other configurations, which can take days to train on a dataset. Initially, the parameters listed in Table 1 were chosen to start the optimization study on the FNN, as it is one of the fastest methods. After testing these values, it was possible to see how each variable behaved. During the tests, the time spent to test a batch with a kernel size of 400 compared to a kernel size of 50 was over 12-times greater with minimal accuracy changes. Thus, all tests using kernel sizes of 400 were discarded as being too time-consuming for minimal results. In Figure 8, it is possible to see that, for an algorithm depth of one hidden layer, the batch size had a small influence on the time per training, while there was no variation in the accuracy. Therefore, a batch size of 128 was chosen, as it was the fastest in both cases with comparable results. The number of hidden layers was tested together with multiple values of the kernel and batch sizes, and the ones with the shortest training times and highest accuracy were chosen. To compare the influence of the number of hidden layers, three tests were conducted with a constant batch size of 128, as defined previously, and a varying kernel size between 50 and 100.