Abstract
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.
Highlights
Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is more than ever, in a continuous transformation
The dynamic Bayesian networks (DBN) was studied as an assembly step predictor
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
Summary
Cyber-physical systems produce major changes in entire industries, especially in the factory area, where there is an ever-increasing discussion about the role of operators in the production processes. More complex assembly support systems use the information about the current state of the system to predict future human actions [20,21,22]. The use case from [20] predicts the assembly state to detect faults and mistakes in a predefined and ordered assembly process This is achieved using an end-to-end neural network that takes as input only images of the assembled product without any other information about the operator. In [29], the authors use DBN for web search ranking They state that the page position affects the number of times the page is clicked: the lower the position of the page, the less likely the user is to click on that page.
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