# Towards an Assembly Support System with Dynamic Bayesian Network
## 5 Conclusions  
In this work, the DBN was studied as an assembly step predictor. The utilization of the DBN in an assembly assistance station for the guidance of the workers is an original contribution. The DBN was optimally configured and compared with other existing prediction methods in terms of prediction accuracy, prediction rate, and coverage. The DBN-based assembly prediction method was validated on a dataset composed of the assemblies and the human characteristics of 68 trainees and will be integrated into the control system of an existing manual assembly training station. The evaluation results have shown that the DBN is able to provide the highest prediction accuracy, with 50% of its predictions being correct, at the expense of a lower prediction rate (51%) and coverage (26%). As we are interested in including the best performing assembly modeling method into our human-oriented assembly assistance system, we intend to further study the applicability of the A* algorithm and hidden Markov models. Finally, the method with the best preliminary results will be validated in an industrial environment. In this case, we expect a simplification in the data gathering of user characteristics, human workers having their own profile with no need for questionnaires. The factory workers must be familiarized with the assembly assistance system before using it in the manufacturing process. Moreover, an industrial context allows a large amount of real-world data to be continuously collected that will improve the prediction accuracy by continuous learning for a certain product. For each new product, a new encoding is necessary. This implies that the proposed method must be applied separately for each product. Any modification in feature significance due to the larger dataset will not require changes to any of the above-mentioned algorithms.  
Figure 12: Coverage with the mood variable trough k-fold validation.  
**Author Contributions:** Conceptualization, A.G. and C.-B.Z.; methodology, A.G. and S.-A.P.; software, S.-A.P.; validation, S.-A.P.; formal analysis, A.G., S.-A.P. and C.-B.Z.; investigation, A.G. and S.-A.P.; resources, C.-B.Z.; data curation, S.-A.P. and A.G.; writing--original draft preparation, S.-A.P., A.G., A.M., C.-B.Z. and M.G.; writing--review and editing, S.-A.P., A.G., A.M., C.-B.Z. and M.G.; visualization, S.-A.P. and A.G.; supervision, A.G. and C.-B.Z.; project administration, C.-B.Z.; funding acquisition, C.-B.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a Hasso Plattner Excellence Research Grant (LBUS-HPI-ERG-2020-03), financed by the Knowledge Transfer Center of the Lucian Blaga University of Sibiu.  
**Institutional Review Board Statement:** All the experiments presented and used in this study were approved by the Research Ethics Committee of Lucian Blaga University of Sibiu (No. 3, on 9 April 2020).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.