Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Abstract

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

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

“…In [7], it was demonstrated that, through gradient boosted decision trees (GBDT), the best coverage of the assembly sequences could be obtained. In [72], an informed tree search supported by a Markov chain (A* algorithm with first order Markov model) was studied. The proposed method outperformed the decision trees (DT).…”

“…Compared to the operator's reaction speed, the system is orders of magnitude faste (real time), minimizing the wait time for the operator between the time the current assem bly task is finished and the time the next assembly task instruction is provided. More in formation about the concept of the prototype or other implementation details can be found in [70][71][72].…”

Green Energy and Environmental Impact on the Industrial Sector in 33 High-Income Countries

“…Based on data gathered between 2010 and 2020, Gellert et al (2022) created projections for the industrial sector using the Markov approach. They asserted that hydropower represented Romania's most recent and cutting-edge renewable energy source.…”

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems

“…Yaghoubi et al [ 11 ] introduced quantitative tools using the Markov method and improved the quality of the oil product line. The Markov model applications also include battery production systems [ 12 ], machine tool modeling [ 13 ], k-out-of-n systems [ 14 , 15 ], reconfigurable production systems [ 16 ], maintenance policy [ 17 ], re-entrant production systems [ 18 ], primary care [ 19 ], rolling bearing monitoring [ 20 ], industrial network models [ 21 ], assembly assistance systems [ 22 ], laser-based manufacturing process [ 23 ], and the steel production process [ 24 , 25 ].…”