Robust Assembly Assistance Using Informed Tree Search with Markov Chains
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 sugges...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/2/495 |
| _version_ | 1797490527985205248 |
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| author | Arpad Gellert Radu Sorostinean Bogdan-Constantin Pirvu |
| author_facet | Arpad Gellert Radu Sorostinean Bogdan-Constantin Pirvu |
| author_sort | Arpad Gellert |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:34:16Z |
| format | Article |
| id | doaj.art-3793e6d97d6d4961a7a731675c2829ad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:34:16Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3793e6d97d6d4961a7a731675c2829ad2023-11-23T15:19:37ZengMDPI AGSensors1424-82202022-01-0122249510.3390/s22020495Robust Assembly Assistance Using Informed Tree Search with Markov ChainsArpad Gellert0Radu Sorostinean1Bogdan-Constantin Pirvu2Computer 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, RomaniaManual 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.https://www.mdpi.com/1424-8220/22/2/495assembly assistance systemstraining stationssmart manufacturingIndustry 4.0digital transformationinformed tree search |
| spellingShingle | Arpad Gellert Radu Sorostinean Bogdan-Constantin Pirvu Robust Assembly Assistance Using Informed Tree Search with Markov Chains Sensors assembly assistance systems training stations smart manufacturing Industry 4.0 digital transformation informed tree search |
| title | Robust Assembly Assistance Using Informed Tree Search with Markov Chains |
| title_full | Robust Assembly Assistance Using Informed Tree Search with Markov Chains |
| title_fullStr | Robust Assembly Assistance Using Informed Tree Search with Markov Chains |
| title_full_unstemmed | Robust Assembly Assistance Using Informed Tree Search with Markov Chains |
| title_short | Robust Assembly Assistance Using Informed Tree Search with Markov Chains |
| title_sort | robust assembly assistance using informed tree search with markov chains |
| topic | assembly assistance systems training stations smart manufacturing Industry 4.0 digital transformation informed tree search |
| url | https://www.mdpi.com/1424-8220/22/2/495 |
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