From lab to industry: lessons learned from the evaluation of augmented and virtual reality use cases in the Austrian manufacturing industry

Figures & data

Figure 1. (a) distribution of industrial AR applications per industrial segments (b) distribution of the industrial applicability of the primary industrial AR studies (de Souza Cardoso et al., 2020).

Figure 2. Comparison of the number of criteria evaluated and number of participants in the user study, based on 29 studies.

Table 1. Description of the use cases and used devices.

ID	Short use case description	Technology	Main Benefit
1_Notepad	A digital remote support notepad to collect requirements on site at the customer’s premises. Information like images, videos and distances are captured and can be geotagged in rooms or placed on machines and objects using digital markers. By digitising information on the spot and associating it directly with the object, the risk of data and information loss is significantly reduced.	AR (open for Android smartphone/tablet)	Increased data quality, improved information sharing, and saved working time
2_Remote	Remote support for surveyors and measurement technicians that can connect to better trained, rare experts. In this way, the expert’s knowledge can be deployed more efficiently because they do not have to be on site themselves especially since it is often not known in advance which expert is needed (details in Aschauer et al. (2021)).	AR (open for Android smartphone/tablet)	Time savings due to problem-solving support for lower-skilled employees on construction sites, fewer business trips as a consequence
3_Remote	Customers can connect to the back-office for troubleshooting support using anchored annotations. This early clarification process aims to determine whether additional Materials, not readily available to the customer, or the expertise of a specialist is required. By doing so, it helps conserve resources and empowers the customer to resolve issues independently whenever possible (details in Aschauer et al. (2021)).	AR (open for Android smartphone/tablet, Vuzix M300)	Increased productivity, savings in travel costs
4_Remote	Remote assistance and guidance with AR support that solves the description problem using only spoken language. Additionally, real-time data and relevant documents are displayed, and the process is recorded, which saves the need for extensive post-processing and documentation effort (details in Aschauer et al. (2021)).	AR (open for Android smartphone/tablet)	Saving business trips, faster problem solving
5_Kitting	AR support for filling kit boxes with material for the assembly. By scanning the kit box, the system recognizes which materials are required for the assembly, ensuring that all necessary components are available. In addition to documenting that all parts have been scanned and placed in the box, pre-assembly steps are also displayed, such as tightening screws to a specific torque.	AR (HMT-1)	Increased productivity and quality by separating assembly and logistics activities and providing AR support for kitting
6_Info	In the food industry, strict standards are in place to ensure safety and quality. Therefore, machines must undergo regular maintenance according to precise schedules. Displayed in AR the employees receive important information (setup and cleaning processes, etc.) depending on the context.	AR (open for Android smartphone)	Better documentation of actual processes, quality improvement, time-saving by quality improvement, guarantee that control is done, documentation
7_Maintenance	AR system to speed up the maintenance process with AR by leaving virtual notes. As a result, the maintenance technician no longer needs to gather descriptions, facts, and data beforehand. Instead, all relevant information is readily available on-site, streamlining the maintenance process and improving overall efficiency.	AR (open for iOS smartphone + HoloLens application for a more precise initial placement of mobile anchors)	Improve the quality of documentation. No direct quote regarding the main benefits
8_Maintenance	Support of a service technician in the maintenance of vehicles using “Dynamic Content-based guidance.”	AR (open for Android tablet)	Improved work efficiency
9_Visualisation	The employee at the production line is presented with location and time-specific checklists, as well as target and actual values of the process. Additionally, all actions performed are logged and documented, providing a comprehensive record of the production activities.	AR (open for Android smartphone, HoloLens)	Improved work efficiency
10_Support	Technicians receive support in line production and testing of machines: plans in 3D, 2D electrical plans, and checklists	AR (open for Android smartphone/tablet)	Further potential is found in the process through process analysis
11_Assembly	The mobile app displays design data (drawings, parts lists) for the corresponding component during assembly.	AR (open for Android tablet)	Reduction of assembly errors due to incorrectly handled bolted joints
12_Identification	Metal profiles are optically identified (partially) automatically, and the results are visualized to ensure that the employee classifies the profile correctly.	AR (open for Android smartphone)	Improved quality control
13_Training	VR training application for geared motors, the teaching of realistic assembly processes, and training content. Moreover, the system can also display the inner workings of the gearbox, enabling employees to gain a better understanding of the product (details in Niedermayr and Wolfartsberger (2021)).	VR (HTC Vive + 2 Controller)	The process is less disturbed by new employees, saving of training personnel, an increase in quality

Figure 3. Evaluation model for industrial AR/VR systems, edited and translated from (Zigart, 2022).

Figure 4. Examples of (a) “7_Maintenance” and (b) “13_Training” use cases.

Figure 5. (a) distribution of applications per industrial segments of the manufacturing use cases in our research project (b) distribution of the industrial applicability of the manufacturing use cases in our research project.

Figure 6. Quality results from interviews.

Figure 7. Task load based on NASA-RTLX; a comparison of users (n = 22) and novices (n = 47).

Figure 8. Usability based on SUS index; a comparison of users (n = 22) and novices (n = 47).

de Souza Cardoso, L. F., Mariano, F. C. M. Q., & Zorzal, E. R. (2020). A survey of industrial augmented reality. Computers & Industrial Engineering, 139, 106159. https://doi.org/10.1016/j.cie.2019.106159

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Aschauer, A., Reisner-Kollmann, I., & Wolfartsberger, J. (2021). Creating an open-source augmented reality remote support tool for industry: Challenges and Learnings. Procedia Computer Science, 180, 269–279. https://doi.org/10.1016/j.procs.2021.01.164

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Niedermayr, D., & Wolfartsberger, J. (2021). Design and evaluation of a virtual training environment for industrial assembly tasks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3862367

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Zigart, T. (2022). Entwicklung eines multikriteriellen Evaluierungsmodells für industrielle Assistenzsysteme [TU Wien]. https://doi.org/10.34726/HSS.2022.98440

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