## 4 Conclusions and Outlook  
The main objective of this study is to contribute to the current research by providing a holistic view of AR systems and AR-based applications in manufacturing, especially focusing on shop-floor processes that require the intensive involvement of operators' activities, such as assembly, maintenance and quality control from the year 2010 to 2021. Thus, this review fills an essential gap in the quality sector and provides a systematic model for the further implementation of AR technology to enhance and support Quality 4.0 in the future. The main contributions of the study are based on the systematic literature review, which has answered the five research questions relating to AR-based applications in the quality sector within a manufacturing context. The conclusions are drawn as follows:  
Firstly, quality control and quality-relevant activities themselves are important to ensure customer specification. However, quality is a special sector that belongs to non-value-added activities for the functionalities of the product. Thus, the fewer errors and the less process time for these quality control and management activities, the more resources (cost, time, human, etc.) can be utilized. In this regard, the advantages of AR technology could be applied to supporting the reduction of human errors and process time, as well as in-line error-prone process controlling. Although AR-based applications benefit the quality sector in several scenarios, there are also some drawbacks. Therefore, before the implementation of AR technology as a solution for quality sector, pre-implementation  
Figure 16: Methodology to design and develop long-term AR-based solution for quality sector adapted from [196]. evaluation is essential to gain insights into which specific cases AR should be utilized in, how the technology could be integrated, whether employing AR is a long-term solution, etc. Secondly, AR technology in manufacturing comes to a point that implementation of software and hardware has improved over time and has gradually reached the essential maturity for long-term industrial AR solutions. However, the current barrier to shop-floor level implementation is user acceptance, which has a long-term impact on the efficiency of the integration of AR solutions in manufacturing. Thus, when it comes to creating a long-term AR-based solution for the quality sector, all relevant elements of AR systems must be systematically evaluated at each step of the design and development process. The model in Figure 16 provides a comprehensive approach to address all impact factors, ensuring that a robust and practical AR-based solution is established step by step. Finally, there are several available software development toolkits and hardware devices that have been improved over time to support the development of AR applications in manufacturing. In order to know which ones are appropriate for a specific AR-based solution to support quality enhancement activities, the working environment conditions need to be considered as well as the requirements in terms of cost, time and effectiveness. By considering all these factors using QFD-AHD, the selection of suitable hardware devices, SDKs and tracking methods could be made in a holistic way. As the result of the SLR and the analysis of current AR technology development, the potential research areas on the subject of AR applied in the quality sector in industry 4.0 context could be one of the following topics:  
* Transformation of a traditional quality Lean tool to a virtual quality tool by identifying and implementing AR technology when it is feasible. * Integration of AR to assist manual and automatic metrology activities to prevent human errors, reduce setup time, ensure the accuracy of metrology data, etc. * Standardization of quality-relevant knowledge representation and quality data formats to make AR systems and manufacturing information systems compatible.