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...

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Autores principales:	Henna Tiensuu, Satu Tamminen, Esa Puukko, Juha Röning
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	explainable AI machine learning GBM smart decision support data driven manufacturing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/5c3d51f894944326a7a553709b4a204f
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spelling	oai:doaj.org-article:5c3d51f894944326a7a553709b4a204f2021-11-25T16:40:06ZEvidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing10.3390/app1122108972076-3417https://doaj.org/article/5c3d51f894944326a7a553709b4a204f2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10897https://doaj.org/toc/2076-3417This 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.Henna TiensuuSatu TamminenEsa PuukkoJuha RöningMDPI AGarticleexplainable AImachine learningGBMsmart decision supportdata driven manufacturingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10897, p 10897 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	explainable AI machine learning GBM smart decision support data driven manufacturing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	explainable AI machine learning GBM smart decision support data driven manufacturing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Henna Tiensuu Satu Tamminen Esa Puukko Juha Röning Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing
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.
format	article
author	Henna Tiensuu Satu Tamminen Esa Puukko Juha Röning
author_facet	Henna Tiensuu Satu Tamminen Esa Puukko Juha Röning
author_sort	Henna Tiensuu
title	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_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_sort	evidence-based and explainable smart decision support for quality improvement in stainless steel manufacturing
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5c3d51f894944326a7a553709b4a204f
work_keys_str_mv	AT hennatiensuu evidencebasedandexplainablesmartdecisionsupportforqualityimprovementinstainlesssteelmanufacturing AT satutamminen evidencebasedandexplainablesmartdecisionsupportforqualityimprovementinstainlesssteelmanufacturing AT esapuukko evidencebasedandexplainablesmartdecisionsupportforqualityimprovementinstainlesssteelmanufacturing AT juharoning evidencebasedandexplainablesmartdecisionsupportforqualityimprovementinstainlesssteelmanufacturing
_version_	1718413078022848512

Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing

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