# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 6 Conclusions  
In this work, the manual assembly of the Dougong structure supported by AR interactive contents was studied. The frameworks for two computer-assisted systems that have different degrees of intelligence were designed. Both systems consisted of two categories of assisted functions: part search and assembly demonstration. A series of manual assembly experiments were conducted to compare three assistance methods (paper, 3D viewer, and AR) in terms of assembly efficiency and accuracy. The experimental results were analyzed using both objective and subjective measures. The former included the time spent in each of the six steps: part recognition, part fetching, part confirmation, assembly confirmation, task recognition, and assembly. A second objective measure estimated the number of errors in each type: part position, part orientation, part fetching error, and incomplete  
Figure 14: Average NASA-TLX scores of the three forms. assembly. NASA-TLX questionnaires and interviews with the subjects provided the subjective assessment. Essential experimental findings include:  
* Traditional paper-based instructions only show part models in a single view and their proportion to actual components are ambiguous. Although participants expressed no difficulties in determining the orientation and position from the paper-based instructions, they committed more position and orientation errors with the paper than the AR system. People may well understand the instruction drawings in 2D and the spatial relationship of different parts shown in one single view. Such a visual perception does not necessarily assure the success of a real assembly task, which often requires 3D reasoning from multiple view angles. * The number of errors made by the AR-based system was the least. This result may be achieved with the part and assembly confirmation functions. Automatic result verification is thus a useful functional feature for computer-assisted assembly systems. Implementation of this feature should consider the assembly time, errors, and system workload simultaneously. The current design needs to be improved to reduce the computational time required by object recognition. To apply deep learning for pose estimation of 3D models from a single RGB image is a feasible solution. Traditional template matching methods such as LINEMOD [22] is also applicable. * Most subjects considered the AR-based system useful and intuitive in assisting the manual assembly of the Dougong models. The interactive display of 3D models allowed them to visualize the part details by freely adjusting the view angle. They agreed that the part and assembly confirmation functions can prevent the users from taking the wrong components. * To incorporate the similar confirmation functions into the 3D viewer or paper-based method is worth pursuing. The implementation would be problematic with potentially poor process flow or usability. AR serves as a more effective interface in this regard. In this study, the practicality of AR in assisting manual assembly of complex structures was verified. However, the subjects suggested several functional improvements to the current AR functions after the experiments. The object recognition process needs to be shortened to enhance the assembly efficiency and user's workload. Adopting AR goggles may be able to reduce the physical stress caused by the current system. In future works, the assisted assembly functions can be investigated using the see-through video mode (goggles) versus the monitor-based mode (screen). It would be interesting to compare learnability after the assembly experience using different methods. Conceptualization, C.-H.C.; Data curation, C.-J.L. ; Investigation, C.-H.C. and C.-J.L. ; Methodology, C.-H.C. and S.-C.L. ; Software, C.-J.L. ; Validation, C.-J.L. ; Writing--original draft, C.-H.C.; Writing--review & editing, S.-C.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Taiwan MOST grant number 108-2221-E-007 -007 -MY3.