# Effect of Augmented Reality Support on Quality Inspection of Welded Structures
## 6 Conclusions  
The conducted research was devoted to the problem of weldment quality control in industrial processes. Inspection tasks are generally among the activities that do not add value to the product but are nevertheless an essential part of all industrial processes. There is a trend in industry towards ever-increasing demands for an error-free quality level, so industrial inspection needs to be carried out with the highest possible efficiency.  
With the development of the fourth industrial revolution, the possibilities of automation are also gradually developing. While for some industries a higher degree of automation is possible, for other types of production, a high degree of automation is not appropriate. These include small batch production, products with short life cycles, or a high frequency of technical changes or design modifications.  
This research therefore focuses on investigating the possibility of introducing a lower level of automation for these types of production. In the chosen level of automation, the assessment and decision-making process is inspector-dependent. However, the process of requirements interpretation, part assessment, and decision making are supported by the technological solution. In the case of this research, this technological solution is augmented reality support.  
The experiment, the statistical evaluation of the experiment data, and the results of hypothesis testing confirmed that by using the proposed AR control approach, it is possible to increase the efficiency of performing weldment inspection in an industrial process while reducing the mental workload perceived by users. The results suggest that the use of an AR solution may also lead to a reduction in error rates. However, more extensive research would be required to allow for statistical confirmation.  
A possible direction for future research is to look more closely at the potential for the detailed visual inspection of individual welded joints. This research would focus on the possibility of comparing quality requirements with inspected weld shape, which might allow for the continuous detection of areas that do not fall within a given quality class. The use of such solution would be particularly applicable to single-unit or large-scale structures, as opposed to vision systems that can be installed on production lines.  
Another potential direction for future research would be more detailed automated weld inspection, requiring a combination of AR and advanced deep learning methods such as localisation, detection, object classification, or image segmentation. The weld quality could be assessed using a neural network trained on a large amount of image material to detect, evaluate, and identify the type of defects in the material structure through image segmentation and abnormal object detection. The classification of a sufficiently large number of images containing texture anomalous welds would be essential for this research.  
Conceptualisation, K.H., P.H. and P.K.; methodology, K.H. and P.H.; validation, K.H.; formal analysis, P.H. and K.H.; investigation, K.H. and P.H.; resources, K.H., P.H. and P.K.; writing--original draft preparation, K.H.; writing--review and editing, P.H., K.H. and P.K.; visualisation, K.H.; supervision, P.H. and P.K.; project administration, P.H. and K.H.; and funding acquisition, P.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Internal Science Foundation of the University of West Bohemia under grant SGS-2023-025 \"Environmentally sustainable production\".  
The study was approved by the internal university ethics committee (ZCU 012668/2023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.