Towards an Assembly Support System with Dynamic Bayesian Network

Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bayesian network implementation can accurately recommend the next assembly step in 50% of the cases, but to the detriment of the prediction rate.

Automated summary

This study analyzes the possibility of using dynamic Bayesian networks to predict assembly steps in the manufacturing industry and develops an assembly assistance training system to support human workers. The experimental results show that dynamic Bayesian networks have high accuracy in predicting new patterns, but at the expense of prediction rate.