Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing
This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/22/10897 |
| _version_ | 1797511280864526336 |
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| author | Henna Tiensuu Satu Tamminen Esa Puukko Juha Röning |
| author_facet | Henna Tiensuu Satu Tamminen Esa Puukko Juha Röning |
| author_sort | Henna Tiensuu |
| collection | DOAJ |
| description | This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement. |
| first_indexed | 2024-03-10T05:43:09Z |
| format | Article |
| id | doaj.art-5c3d51f894944326a7a553709b4a204f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:43:09Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5c3d51f894944326a7a553709b4a204f2023-11-22T22:20:36ZengMDPI AGApplied Sciences2076-34172021-11-0111221089710.3390/app112210897Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel ManufacturingHenna Tiensuu0Satu Tamminen1Esa Puukko2Juha Röning3Biomimetics and Intelligent Systems Group, University of Oulu, P.O. Box 4500, FI-90014 Oulu, FinlandBiomimetics and Intelligent Systems Group, University of Oulu, P.O. Box 4500, FI-90014 Oulu, FinlandOutokumpu Stainless Oy, 95490 Tornio, FinlandBiomimetics and Intelligent Systems Group, University of Oulu, P.O. Box 4500, FI-90014 Oulu, FinlandThis article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement.https://www.mdpi.com/2076-3417/11/22/10897explainable AImachine learningGBMsmart decision supportdata driven manufacturing |
| spellingShingle | Henna Tiensuu Satu Tamminen Esa Puukko Juha Röning Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing Applied Sciences explainable AI machine learning GBM smart decision support data driven manufacturing |
| title | Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing |
| title_full | Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing |
| title_fullStr | Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing |
| title_full_unstemmed | Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing |
| title_short | Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing |
| title_sort | evidence based and explainable smart decision support for quality improvement in stainless steel manufacturing |
| topic | explainable AI machine learning GBM smart decision support data driven manufacturing |
| url | https://www.mdpi.com/2076-3417/11/22/10897 |
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