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 journalArticleScientificpeer-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 languageEnglish
Pages (from-to)291-313
Number of pages23
JournalInternational Journal of Industrial and Systems Engineering
Volume33
Issue number3
DOIs
Publication statusPublished - 28 Oct 2019
MoE publication typeA1 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

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