University of Oulu

Tiensuu, H.; Tamminen, S.; Puukko, E.; Röning, J. Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing. Appl. Sci. 2021, 11, 10897. https://doi.org/10.3390/app112210897

Evidence-based and explainable smart decision support for quality improvement in stainless steel manufacturing

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Author: Tiensuu, Henna-Riikka1; Tamminen, Satu1; Puukko, Esa2;
Organizations: 1Biomimetics and Intelligent Systems Group, University of Oulu, P.O. Box 4500, FI-90014 Oulu, Finland
2Outokumpu Stainless Oy, 95490 Tornio, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021121060049
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2021
Publish Date: 2021-12-10
Description:

Abstract

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.

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Series: Applied sciences
ISSN: 2076-3417
ISSN-E: 2076-3417
ISSN-L: 2076-3417
Volume: 11
Issue: 22
Article number: 10897
DOI: 10.3390/app112210897
OADOI: https://oadoi.org/10.3390/app112210897
Type of Publication: A1 Journal article – refereed
Field of Science: 112 Statistics and probability
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
GBM
Funding: This research was funded by Flexible and Adaptive Operations in Metal Production project (FLEX) project with project number 6905/31/2016.
Copyright information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/