## 3 Results and Discussion
### Answering RQ1 and RQ2
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Criteria** & **Number or Articles** & **Global Percentage (\%)** \\ \hline _Application field_ & _200_ & _100_ \\ \hline Maintenance & 38 & 19.0 \\ \hline Assembly & 75 & 37.5 \\ \hline Quality & 32 & 16.0 \\ \hline Others & 12 & 6.0 \\ \hline General manufacturing context & 43 & 21.5 \\ \hline _Layer 1_ & _Concept \& Theory_ & _127_ & _63.5_ \\ \hline
1.1 & Algorithm \& Modelling & 47 & 23.5 \\ \hline
1.2 & Conceptual framework & 48 & 24 \\ \hline
1.3 & Evaluation framework & 9 & 4.5 \\ \hline
1.4 & Tech adoption & 57 & 28.5 \\ \hline _Layer 2_ & _Implementation_ & _150_ & 75 \\ \hline
2.1 & _Software_ & _150_ & 75 \\ \hline
2.1.1 & Content design & 40 & 20 \\ \hline
2.1.2 & Interaction design & 95 & 47.5 \\ \hline
2.1.3 & Agent-based AR & 4 & 2 \\ \hline
2.1.4 & Knowledge-based AR & 12 & 6 \\ \hline
2.2 & _Hardware_ & _148_ & 74 \\ \hline \multirow{2}{*}{In-situ} & Spatial display/Projector & 19 & 9.5 \\ \cline{2-2}  & Monitor/Large screen & 34 & 17 \\ \hline \multirow{2}{*}{Mobile} & HHD & 42 & 21 \\ \cline{2-2}  & HMD & 50 & 25 \\ \hline \hline \multicolumn{2}{c}{Multimodal} & 16 & 8 \\ \hline \multicolumn{2}{c}{Others} & 1 & 0.5 \\ \hline _Layer 3_ & _Evaluation_ & 62 & _31_ \\ \hline
3.1 & Usability & 13 & 6.5 \\ \hline
3.2 & Effectiveness & 24 & 12 \\ \hline
3.3 & Effectiveness + Usability & 27 & 13.5 \\ \hline _Layer 4_ & _Industry adoption_ & _153_ & _76.5_ \\ \hline \multirow{2}{*}{Tested in industry} & \multirow{2}{*}{Field experiment} & \multirow{2}{*}{25} & \multirow{2}{*}{12.5} \\ \cline{2-2}  & Laboratory experiment & 69 & 34.5 \\ \cline{2-2}  & Pilot project & 59 & 29.5 \\ \hline _Layer 5_ & _Intelligent AR_ & _20_ & _10_ \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Number of articles classified by the framework. found in 31% (62 articles) of the publications. 25 works, or 12.5% of the total, perform field experiments and have significant industry adoption context (Layer 4). An interesting point is that 20 articles, or 10% of the total, have contributed proof-of-concept or a conceptual framework supporting the current stage and further integration of intelligent elements for AR solutions (Layer 5). These five layers' percentage distribution have already illustrated a holistic view of the ongoing stage and the trend of AR-based application for the manufacturing context. They also depicted a general view of what AR-based solutions for manufacturing context have accomplished (see Figure 9). The AR technology has rapidly evolved and reached its mature point to be integrated into manufacturing, equipping operators with immersive interaction tools on the shop-floor level and providing essential manufacturing information for decision making in a short time. Regarding Layer 1, \"AR Concept and Theory\", the publications can be categorized into four subjects (see Table 9 and Figure 10). This layer is dedicated to the concept of how AR adoption benefits in solving problems in one specific field of manufacturing: the new theories and fundamentals to build and utilize AR for manufacturing contexts. The algorithm is a crucial element in developing an AR system. It consists of studies relevant to Artificial Intelligent methodology, establishing the base for AR to grow into intelligent systems [191]. A conceptual framework provides a general view of what the AR systems are and how they can be implemented. It may be relevant to the systems' capabilities, the system functions of the AR user interface, the system data flow or system management [190]. The evaluation framework forms the fundamental of heuristic guidelines either for the evaluating and selecting of AR elements for implementation or for analyzing and evaluating the usability of AR solutions in the context of manufacturing.