Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

Abstract

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 some cases all the other models. Nevertheless, an assembly assistance system meant to support factory workers needs to embed multiple models to exhibit valuable predictive capabilities.

Problems of forecasting the length of the assembly cycle of complex products realized in the MTO (make-to-order) model

“…The process based on the MTO model begins when an order is received from a customer for a specific product. Only after the order is received is the raw materials needed to fulfill the order ordered (or their reservation if they are in stock) ordered [12]. Production planning, purchase of materials or scheduling of production tasks in the short term take place on the basis of orders received.…”