Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment
Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/4/499 |
| _version_ | 1797604606528716800 |
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| author | Constantine A. Kyriakopoulos Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris |
| author_facet | Constantine A. Kyriakopoulos Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris |
| author_sort | Constantine A. Kyriakopoulos |
| collection | DOAJ |
| description | Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of a product’s physical parameters from an acceptable range of values in a manufacturing line is a process that can assist in delivering consistent and high-quality output. Costs are saved by avoiding bursts of defective products in the pipeline’s output. An innovative framework for the early detection of a product’s physical parameter divergence from a specified quality range is designed and evaluated in this study. This framework is based on learning automata to find the sequences of variables that have the highest impact on the automated sensor measurements that describe the environmental conditions in the production line. It is shown by elaborate evaluation that complexity is reduced and results close to optimal are feasible, rendering the framework suitable for deployment in practice. |
| first_indexed | 2024-03-11T04:49:08Z |
| format | Article |
| id | doaj.art-7ccc1d556f634d4b9c72bcbded2eddcb |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-11T04:49:08Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-7ccc1d556f634d4b9c72bcbded2eddcb2023-11-17T20:09:28ZengMDPI AGMachines2075-17022023-04-0111449910.3390/machines11040499Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing EnvironmentConstantine A. Kyriakopoulos0Ilias Gialampoukidis1Stefanos Vrochidis2Ioannis Kompatsiaris3Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Rd, P.O. Box 60361, Thermi, GR 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, 6th km Charilaou-Thermi Rd, P.O. Box 60361, Thermi, GR 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, 6th km Charilaou-Thermi Rd, P.O. Box 60361, Thermi, GR 57001 Thessaloniki, GreeceCentre for Research and Technology Hellas, 6th km Charilaou-Thermi Rd, P.O. Box 60361, Thermi, GR 57001 Thessaloniki, GreeceProduction lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of a product’s physical parameters from an acceptable range of values in a manufacturing line is a process that can assist in delivering consistent and high-quality output. Costs are saved by avoiding bursts of defective products in the pipeline’s output. An innovative framework for the early detection of a product’s physical parameter divergence from a specified quality range is designed and evaluated in this study. This framework is based on learning automata to find the sequences of variables that have the highest impact on the automated sensor measurements that describe the environmental conditions in the production line. It is shown by elaborate evaluation that complexity is reduced and results close to optimal are feasible, rendering the framework suitable for deployment in practice.https://www.mdpi.com/2075-1702/11/4/499production linesmart manufacturingreinforcement learningquality diagnosispredictionheuristics |
| spellingShingle | Constantine A. Kyriakopoulos Ilias Gialampoukidis Stefanos Vrochidis Ioannis Kompatsiaris Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment Machines production line smart manufacturing reinforcement learning quality diagnosis prediction heuristics |
| title | Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment |
| title_full | Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment |
| title_fullStr | Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment |
| title_full_unstemmed | Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment |
| title_short | Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment |
| title_sort | adaptive quality diagnosis framework for production lines in a smart manufacturing environment |
| topic | production line smart manufacturing reinforcement learning quality diagnosis prediction heuristics |
| url | https://www.mdpi.com/2075-1702/11/4/499 |
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