# Effect of Augmented Reality Support on Quality Inspection of Welded Structures
## 4 Results
### Efficiency  
To evaluate the effectiveness of the task performance in the experiment, a two-way analysis of variance, abbreviated as ANOVA, followed by an analysis of residuals [70; 71] and a post hoc Tukey test [72] was performed. ANOVA is a form of statistical analysis that divides the total variance in a set of data into sections that are connected to certain factors causing variance in order to test hypotheses related to the model's parameters [73; 74; 75]. Figure 8 shows a graph with the means and standard errors for the ANOVA analysis. The graph shows three pairs of bars, with each pair representing one group of data. The first column in each pair represents the G1 group, while the second column represents G2. The  
Figure 7: Tests of data normality from inspection using AR approach. columns are divided depending on the approach used, namely the AR approach (first group of columns) and the traditional paper-based approach (second group of columns). The third set of columns includes the inspection time with the conventional approach, which, unlike the second set of columns, also includes the documentation handling time. The standard error is reported in the graph as a measure of the variability of the means between the groups, along with the standard error in this graph, which allows for a visual comparison of the stability and precision of the averages. The graph of averages shows that G1 using AR achieves the lowest average task completion times. G2 with AR support shows the second-lowest average. The results from G2 show a higher value of standard error, indicating some variability in the results. With the support of traditional paper-based methods, both groups show longer average completion times. The difference between the average completion time of the control task for G1 and G2 is higher for the traditional paper-based methods than for AR. In paper-based inspection, the user is forced to divide their attention between the documentation, the part to be inspected, and the inspection and measurement tools. This division of attention leads to increased time for the completion of the inspection, distraction, and the relatively poorer or slower orientation of the inspected piece. In this scenario, higher demands are also placed on the user's measurement and metrology knowledge. It is therefore a task demanding the time, concentration, and expertise of the worker. In the case of the proposed AR approach, the augmented reality interface supports the user in a quick basic orientation of the product. By comparing it with the ideal state of the model, the user gets a quick idea of the desired layout of subassemblies and welded joints, and they are thus able to complete the inspection of the part in a relatively short time, possibly finding an error. At the same time, thanks to the visualisations used, lower demands are placed on the user's professional qualifications or experience in reading and interpreting welding drawings. During the execution of the inspection task during the experiment, most participants showed a gradual decrease in check times. The reason given was that they were able to navigate the drawings and the part itself more quickly when repeatedly inspecting the same type of product. At the same time, however, even the participants with the most extensive experience and professional qualifications stated that, given the complexity of the parts inspected, their similarity, and the total number of product types inspected, they did not reach a state during the experiment where they were able to inspect the part by memory without any supporting documentation. Thus, none of the participants reached a level of part familiarity where the AR approach to inspection would in turn begin to slow them down and negatively affect their performance. However, it is likely that with a different experimental setup, i.e., a different duration for completing the control task or different variability, this learning factor may influence the course and results of the experiment in a different way than in this research.