Abstract
Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors.
Highlights
The Smart Factory vision in Industry 4.0 is a complex system where artefacts collaborate with people, facilitated by a complex exchange of data among all interactants, both in the physical and in the digital world
In this paper a prediction-based assembly support system was presented, which can adaptively direct the workers in their manufacturing activities
The focus is on its prediction module which, in this work, uses the Prediction by Partial Matching (PPM) algorithm to provide choices for the assembly step, which can be helpful especially for inexperienced workers
Summary
The Smart Factory vision in Industry 4.0 is a complex system where artefacts collaborate with people, facilitated by a complex exchange of data among all interactants, both in the physical and in the digital world In this data-driven collaboration environment, human assistance systems play a major role by providing the most suitable information at the right time, as specified in the Operator 4.0 concept [1,2]. Sensors are widely used to detect body position and motion, to recognize facial expressions, and to identify objects Such information, recorded within an assembly station, can be used to recognize the current state of the manufacturing process and to recommend possible assembly steps. As the goal is to integrate the most efficient predictor in the assembly assistance system to support the workers with choices for the assembly step, other methods will be further investigated. The rest of this paper is organized as follows: Section 2 presents related work, Section 3 describes the proposed prediction methods used to recommend the assembly step, Section 4 discusses the evaluation, whereas Section 5 concludes the paper and proposes some further work directions
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