Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions
This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in so…
Prediction-Based Assembly Assistance System
This paper presents the design of a prediction-based assembly assistance system for manual operations and the results obtained on the data collected from experiments of assembling a customizable product. We integrated into the proposed system a Markov predictor improved with a padding mechanism whose role is to recommend the next assembly step and to detect the worker’s errors. The predictor is trained with correct assembly patterns and tested with real assembly/manufacturing data. The proposed predictor improves the coverage and, thus, there is a significantly higher number of assembly steps which are correctly correlated with the real intentions of the workers.
Assembly Process Modeling Through Long Short-Term Memory
This paper studies Long Short-Term Memory as a component of an adaptive assembly assistance system suggesting the next manufacturing step. The final goal is an assistive system able to help the inexperienced workers in their training stage or even experienced workers who prefer such support in their manufacturing activity. In contrast with the earlier analyzed context-based techniques, Long Short-Term Memory can be applied in unknown scenarios. The evaluation was performed on the data collected previously in an experiment with 68 participants assembling as target product a customizable modular tablet. We are interested in identifying the most accurate method of next assembly step prediction…
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 Bay…