## 3 Results and Discussion
### Answering RQ3 to RQ5
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref.**} & **Programming** & **Functionalities** & **3D Content Creation** \\  & & **Language** & **Library/SDK** & **3D Content Creation** \\ \hline
2016 & [185] & C++ & Metaio SDK & N/A \\ \hline
2016 & [202] & C++ &
\begin{tabular}{c} ARToolkit, \\ OpenGL libraries \\ \end{tabular} & N/A \\ \hline
2015 & [68] & N/A & ARToolkit & Solidworks \\ \hline
2015 & [69] & C++ & OpenCV & Coin3D \\ \hline
2015 & [75] & C++ & Qt & N/A \\ \hline
2015 & [95] & C++ & OpenCV & Coin3D \\ \hline
2015 & [98] & N/A &
\begin{tabular}{c} OpenSceneGraph, OpenCV, \\ ARToolkit \\ \end{tabular} & N/A \\ \hline
2015 & [105] & Java & N/A & 3 Ds Max \\ \hline
2015 & [146] & Java, C++ &
\begin{tabular}{c} Microsoft foundation class (MFC), \\ OpenSceneGraph (OSG), ARToolKit, OpenGL \\ \end{tabular} & N/A \\ \hline
2015 & [159] & N/A & OpenGL, ARToolkit & N/A \\ \hline
2015 & [178] & N/A & Unity, Metaio for Oculus Rift & N/A \\ \hline
2015 & [186] & N/A & Unifeye, Metaio SDK & N/A \\ \hline
2014 & [88] & C++ & OWL API, ARToolkit & N/A \\ \hline
2014 & [81] & N/A & OpenCV, OpenInventor & N/A \\ \hline
2014 & [215] & C++ & ARToolKit & N/A \\ \hline
2013 & [55] & N/A & ARToolKitPlus, OpenGL & SolidWorks \\ \hline
2013 & [79] & N/A & ARToolkit & N/A \\ \hline
2013 & [100] & C\#. NET &
\begin{tabular}{c} Unity3D, Vutoria, ROS, \\ JSON library \\ \end{tabular} & N/A \\ \hline
2013 & [148] & C++ & OWL API, ARToolKit & Solidworks \\ \hline
2013 & [149] & C++ & ARToolkit, OpenGL & N/A \\ \hline
2013 & [169] & C++ &
\begin{tabular}{c} OpenSceneGraph, OpenNI library, Windows \\ Forms \\ \end{tabular} & N/A \\ \hline
2013 & [180] & N/A & Unity3D, Zigfu plugin & N/A \\ \hline
2011 & [56] & N/A & OpenCV, OpenGL & N/A \\ \hline
2011 & [156] & N/A & OpenGL & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 16: _Cont._widely used too. However, other similar tests are also available for relevant use cases such as the Subjective Workload Assessment Technique (SWAT) [246], the Unified Theory on Acceptance and Use of Technology (UTAUT) [247], a Likert Scale questionnaire [142], a Computer System Usability Questionnaire (CSUQ) [141], etc. Nevertheless, the standards ANSI 2001 and ISO 9241 [195, 206] are essential when considering metrics to evaluate the usefulness of a developed AR tool via analyzing human performance towards some target acquisition tasks. There are 62 articles (31% of 200 articles in this SLR) that provided rigorous evaluation work, with 13 articles (6.5%) relating to usability evaluation, which is slightly more than half of the number of articles relevant to effectiveness evaluation (24 articles, 12%), and 27 articles (13.5%) conducting both effectiveness and usability evaluation. Considering the nature of AR technology, which mainly enhances user perspectives, usability evaluation should be more frequently employed to heuristically address all potential impacts of the AR system. Thus, more holistic and comprehensive evaluating results can be achieved for further development of the AR system and AR technology in the long term. In summary, this subsection provides a useful evaluation methodology as well as different standards that could be reused to analyze the AR-based solutions for the quality sector later. Table 17 includes and classifies articles that are relevant to the evaluation of AR systems in manufacturing. RQ5: How to develop an AR-based solution for long-term benefits of quality in manufacturing? Quality control procedures or activities relating to the quality aspect in manufacturing frequently include intensive repetitive and precise tasks which are regularly complex and require human involvement. In some findings, it was mentioned that in terms of human error controlling, there is a current bottleneck in AR-assisted manufacturing systems. This relates to the artificial intelligence capabilities of AR systems [114, 189].