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 |
| Number of pages | 23 |
| Journal | International Journal of Industrial and Systems Engineering |
| Volume | 33 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 28 Oct 2019 |
| MoE publication type | A1 Journal article-refereed |
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Keywords
- Data mining
- GBM
- Generalised boosted regression
- Knowledge representation
- Online monitoring
- Product design
- Quality improvement
- Smart decision support
Cite this
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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 -