# Effect of Augmented Reality Support on Quality Inspection of Welded Structures
## 5 Discussion  
This section discusses the results of the experiment, the benefits to industry and science, and recommendations for future research. The research aims to evaluate the influence of the AR-based methodology of visual quality control of weldments on the performance of this control and the mental load of users in a real industrial workplace. The results of the experiment with users show that the use of AR in the quality inspection of weldments leads to an increase in the efficiency of the execution of this task, i.e., a reduction in the execution time of this inspection compared to using printed paper documentation. These results are in line with similar studies that observed an increase in the efficiency of task execution with AR support compared to paper instructions [76; 77; 78] and video instructions [48]. However, some studies have reported the opposite trend, i.e., a decrease in task performance when AR support is involved [79]. The lower efficiency of execution of the assembly operation can be explained by the spatial distribution of instructions and their longer search and orientation during the task. Conversely, the increased efficiency of the control process may be explained by the nature of the design of the experiment. In this experiment, users inspected weldments dimensionally characterised from 300 to 800 mm and 2 kg to 7 kg. It was therefore relatively easy for them to handle these weldments. In contrast to traditional instructions, the inspectors did not have to divide their attention, and by comparing the ideal 3D model displayed over the real weldment, even a less experienced inspector made a relatively quick initial orientation of the part and checked the basic completeness and basic dimensions without having to measure all the dimensions. In this research, a relatively low average error rate, or failure to detect randomly distributed errors on parts, or incorrectly marking a defect-free part as defective, was observed. A lower error rate was observed using the AR approach compared to conventional printed documents. However, for the chosen \(\alpha\) significance level of 0.05, there was no statistically significant decrease in the error rate for the proposed AR solution. In similar studies in the field of assembly research, a decrease in assembly errors was found [80; 81; 82]. In addition to the effect on efficiency and error rate, the involvement of the AR approach was also investigated in terms of the perceived mental workload on the users. This workload was assessed by the standardised NASA TLX questionnaire. During the experiment, users perceived a lower mental workload compared to the conventional paper-based approach. This positive result confirms that the proposed AR approach can reduce the mental workload in weldment quality control by reducing the division of attention between the prescribed design requirements and the real part. This result confirms that it is possible to reduce mental workload, increase user concentration, and promote the efficiency of inspection task execution. Other available studies confirm similar results [81; 82; 83; 84; 85]. In addition to the effectiveness results, the usability and acceptance of the proposed solution were rated positively in the final SUS questionnaires and the post-experiment interviews. The probands preferred the AR solution to the conventional approach with printed documents. The usability of the tool used was rated positively by the probands with an average score of 94.7 on a scale of 0 to 100. This rating corresponds to an A+ level and reflects high user satisfaction with the solution used. The available studies generally agree on an acceptability threshold above 70 points [86; 87]. The probands appreciated the possibility of a quick comparison of the inspected part and the ideal model, and fast orientation in interpreting the requirements. According to some available studies dedicated to AR, experience with modern and innovative technologies may also influence the effectiveness of AR-enabled task execution [81].