# Towards an Assembly Support System with Dynamic Bayesian Network
## 1 Introduction  
Industry 4.0 concepts are surprising the manufacturing industry by requiring continuous transformations due to technological advancement in internet technologies, sensors, and data processing hardware. On the other side, the customers are pressuring the production lines with highly customized, small batch orders. These recent requirements shift the paradigm from a rigid, static factory to more flexible, automated, and intelligent production lines that need to dynamically update the production plan based on customer orders and other events--predicted or unexpected. Even though repetitive or dangerous actions and operations are becoming automated, the human workers are not removed entirely from the manufacturing process. To keep the human and the manual assembly operations in line with the digitalization of the industry, new concepts, such as Operator 4.0 [1], Healthy Operator 4.0 [2], or Assembly 4.0 [3, 4], emerged. Operators are gaining new responsibilities and are helped in the process by continuous training or by receiving real-time support with the help of devices such as smart glasses and augmented reality technology [5, 6, 7]. The manual assembly processes are modeled to be flexible and are monitored with the help of motion capture technologies [8, 9]. All the sensors used for this generate a lot of industrial data that is analyzed with the help of machine learning and artificial intelligence algorithms so they can be further improved and optimized or to identify unusual states of the processes.  
In this work, we present dynamic Bayesian networks (DBN) applied to predict the next assembly step during an assisted manual manufacturing process. The proposed approach will become a part of a prediction module of a larger control system used on a physical assembly training station that can adapt the instruction sequence and content based onwhat the operator is doing at that moment. The control system, described in [10] as part of the digital twin of the assembly training station, consists of multiple sensors that are used to obtain the current state of the assembly, several modules used for prediction and adaptation, together with a large visual interface where the operator sees the output of the control system. Depending on the use case, the next assembly step prediction can be used either to anticipate what the operator will do and prepare in advance for his actions or as hints that the operator can use and follow.  
The DBN relies also on Markov chains for establishing its initial probabilities. The network uses as input data the current assembly state of the product and static characteristics of the human worker, such as the height, the gender, if it wears glasses or not, and the sleep quality in the previous night. The workers' assembly preferences were highly influenced by their gender (e.g., first picked component based on its color). Their height affected the order of picked components, shorter workers picking the components located closer to them first. The sleep quality affects their concentration capabilities, reflected in the number of mistakes made during the assembly. Wearing eyeglasses also influenced the assembly process.  
Additionally, we evaluate the usefulness of applying some dynamic human characteristics, such as the mood in each stage of the assembly process. Our DBN-based assembly prediction method is evaluated on a dataset collected during an experiment involving 68 students, described in [11] and [12]. We will also compare it with our previous work on next assembly step predictors.  
The rest of this paper has the following structure: Section 2 presents the related work, Section 3 describes the DBN-based next assembly step prediction, Section 4 discusses the results, and Section 5 concludes the paper.