Worker assistance systems and assembly process maturity in the prototype and pre-series production

Rupp, Stephan; Müller, Rainer

doi:10.1016/j.promfg.2020.10.199

Cited by 3 publications

(1 citation statement)

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“…An approach for designing better instructions, considering cognition, is tackled in [21], where a novel framework for developing such systems is described. Rupp and Müller presented in [22] a general approach and first implementation of a modular event-driven architecture assistance system to support operators at their workplaces in prototype and pre-series production. The instructions are generated directly from higher planning systems, while the assembly visualization is done in 3D, relying on the drawing database in conjunction with data from the product data management system.…”

Section: Related Workmentioning

confidence: 99%

Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Gellért

Sorostinean

Pirvu

2022

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%