An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems

An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems

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 pr...

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Main Authors: Arpad Gellert, Stefan-Alexandru Precup, Bogdan-Constantin Pirvu, Ugo Fiore, Constantin-Bala Zamfirescu, Francesco Palmieri
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Applied Sciences
Subjects:
assembly assistance system
industry 4.0
prediction by partial matching
smart factory
training station
Online Access:https://www.mdpi.com/2076-3417/11/7/3278
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spelling doaj-d61b30329de14ab19fb4a7c78640e6f92021-04-06T23:03:37ZengMDPI AGApplied Sciences2076-34172021-04-01113278327810.3390/app11073278An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance SystemsArpad Gellert0Stefan-Alexandru Precup1Bogdan-Constantin Pirvu2Ugo Fiore3Constantin-Bala Zamfirescu4Francesco Palmieri5Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, RomaniaComputer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, RomaniaIndustrial Engineering and Management Department, Lucian Blaga University of Sibiu, 550025 Sibiu, RomaniaDepartment of Management Studies and Quantitative Methods, Parthenope University, 80133 Napoli, ItalyComputer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550025 Sibiu, RomaniaDepartment of Computer Science, University of Salerno, 84084 Salerno, ItalyIndustrial 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.https://www.mdpi.com/2076-3417/11/7/3278assembly assistance systemindustry 4.0prediction by partial matchingsmart factorytraining station
collection DOAJ
language English
format Article
sources DOAJ
author Arpad Gellert
Stefan-Alexandru Precup
Bogdan-Constantin Pirvu
Ugo Fiore
Constantin-Bala Zamfirescu
Francesco Palmieri
spellingShingle Arpad Gellert
Stefan-Alexandru Precup
Bogdan-Constantin Pirvu
Ugo Fiore
Constantin-Bala Zamfirescu
Francesco Palmieri
An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems
Applied Sciences
assembly assistance system
industry 4.0
prediction by partial matching
smart factory
training station
author_facet Arpad Gellert
Stefan-Alexandru Precup
Bogdan-Constantin Pirvu
Ugo Fiore
Constantin-Bala Zamfirescu
Francesco Palmieri
author_sort Arpad Gellert
title An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems
title_short An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems
title_full An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems
title_fullStr An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems
title_full_unstemmed An Empirical Evaluation of Prediction by Partial Matching in Assembly Assistance Systems
title_sort empirical evaluation of prediction by partial matching in assembly assistance systems
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-04-01
description 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.
topic assembly assistance system
industry 4.0
prediction by partial matching
smart factory
training station
url https://www.mdpi.com/2076-3417/11/7/3278
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