An online quality monitoring tool for information acquisition and sharing in manufacturing: Requirements and solutions for the steel industry

Satu Tamminen; Eija Ferreira; Henna Tiensuu; Heli Helaakoski; Vesa Kyllönen; Juha Jokisaari; Esa Puukko; Juha Röning

doi:10.1504/IJISE.2019.103442

An online quality monitoring tool for information acquisition and sharing in manufacturing: Requirements and solutions for the steel industry

Satu Tamminen, Eija Ferreira, Henna Tiensuu, Heli Helaakoski, Vesa Kyllönen, Juha Jokisaari, Esa Puukko, Juha Röning

Research output: Contribution to journal › Article › Scientific › peer-review

Abstract

The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.

Original language	English
Pages (from-to)	291-313
Journal	International Journal of Industrial and Systems Engineering
Volume	33
Issue number	3
DOIs	https://doi.org/10.1504/IJISE.2019.103442
Publication status	Published - 28 Oct 2019
MoE publication type	A1 Journal article-refereed

Keywords

Data mining
GBM
Generalised boosted regression
Knowledge representation
Online monitoring
Product design
Quality improvement
Smart decision support

Access to Document

10.1504/IJISE.2019.103442

Fingerprint Dive into the research topics of 'An online quality monitoring tool for information acquisition and sharing in manufacturing: Requirements and solutions for the steel industry'. Together they form a unique fingerprint.

Cite this

Tamminen, S., Ferreira, E., Tiensuu, H., Helaakoski, H., Kyllönen, V., Jokisaari, J., Puukko, E., & Röning, J. (2019). An online quality monitoring tool for information acquisition and sharing in manufacturing: Requirements and solutions for the steel industry. International Journal of Industrial and Systems Engineering, 33(3), 291-313. https://doi.org/10.1504/IJISE.2019.103442

Tamminen, Satu ; Ferreira, Eija ; Tiensuu, Henna ; Helaakoski, Heli ; Kyllönen, Vesa ; Jokisaari, Juha ; Puukko, Esa ; Röning, Juha. / An online quality monitoring tool for information acquisition and sharing in manufacturing : Requirements and solutions for the steel industry. In: International Journal of Industrial and Systems Engineering. 2019 ; Vol. 33, No. 3. pp. 291-313.

@article{44612deccfa742b49186209d9f7c4891,

title = "An online quality monitoring tool for information acquisition and sharing in manufacturing: Requirements and solutions for the steel industry",

abstract = "The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.",

keywords = "Data mining, GBM, Generalised boosted regression, Knowledge representation, Online monitoring, Product design, Quality improvement, Smart decision support",

author = "Satu Tamminen and Eija Ferreira and Henna Tiensuu and Heli Helaakoski and Vesa Kyll{\"o}nen and Juha Jokisaari and Esa Puukko and Juha R{\"o}ning",

year = "2019",

month = oct,

day = "28",

doi = "10.1504/IJISE.2019.103442",

language = "English",

volume = "33",

pages = "291--313",

journal = "International Journal of Industrial and Systems Engineering",

issn = "1748-5037",

publisher = "Inderscience Publishers",

number = "3",

}

Tamminen, S, Ferreira, E, Tiensuu, H, Helaakoski, H , Kyllönen, V, Jokisaari, J, Puukko, E & Röning, J 2019, 'An online quality monitoring tool for information acquisition and sharing in manufacturing: Requirements and solutions for the steel industry', International Journal of Industrial and Systems Engineering, vol. 33, no. 3, pp. 291-313. https://doi.org/10.1504/IJISE.2019.103442

An online quality monitoring tool for information acquisition and sharing in manufacturing : Requirements and solutions for the steel industry. / Tamminen, Satu; Ferreira, Eija; Tiensuu, Henna; Helaakoski, Heli ; Kyllönen, Vesa; Jokisaari, Juha; Puukko, Esa; Röning, Juha.

In: International Journal of Industrial and Systems Engineering, Vol. 33, No. 3, 28.10.2019, p. 291-313.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - An online quality monitoring tool for information acquisition and sharing in manufacturing

T2 - Requirements and solutions for the steel industry

AU - Tamminen, Satu

AU - Ferreira, Eija

AU - Tiensuu, Henna

AU - Helaakoski, Heli

AU - Kyllönen, Vesa

AU - Jokisaari, Juha

AU - Puukko, Esa

AU - Röning, Juha

PY - 2019/10/28

Y1 - 2019/10/28

N2 - The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.

AB - The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.

KW - Data mining

KW - GBM

KW - Generalised boosted regression

KW - Knowledge representation

KW - Online monitoring

KW - Product design

KW - Quality improvement

KW - Smart decision support

UR - http://www.scopus.com/inward/record.url?scp=85074771964&partnerID=8YFLogxK

U2 - 10.1504/IJISE.2019.103442

DO - 10.1504/IJISE.2019.103442

M3 - Article

AN - SCOPUS:85074771964

VL - 33

SP - 291

EP - 313

JO - International Journal of Industrial and Systems Engineering

JF - International Journal of Industrial and Systems Engineering

SN - 1748-5037

IS - 3

ER -