Prediction model based multi-profile monitoring for manufacturing process management

Seung Hwan Park, Cheong Sool Park, Jun-Geol Baek

Research output: Contribution to journalArticle

Abstract

In an advanced manufacturing environment, the analysis of profile data collected from the process equipment is a critical issue in improving process efficiency. In particular, multi-profile monitoring is essential for process control because an advanced manufacturing process consists of numerous pieces of equipment and their related sensors. The main goal of this study is to build a monitoring chart using a Profile Integrated Measure (PIM) from multi-profile data in order to observe an overall condition of various points in the process. To deploy the proposed algorithm, multi-profile data needed to be preprocessed and applied to the prediction model. The PIM is calculated from the prediction model and reflects the relationships between the multi-profile data property, which has normal/abnormal states. The proposed algorithm constructs a model using the PIM of a normal state and identifies the performance of the model. Experiments with the simulation datasets modified from the manufacturing process validate the effectiveness and applicability of the proposed algorithm.

Original languageEnglish
Pages (from-to)394-406
Number of pages13
JournalInternational Journal of Industrial Engineering : Theory Applications and Practice
Volume26
Issue number3
Publication statusPublished - 2019 Jan 1

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Monitoring
Process control
Sensors
Experiments

Keywords

  • Manufacturing process simulation
  • Multi-profile
  • Prediction model
  • Profile integrated measure

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Prediction model based multi-profile monitoring for manufacturing process management. / Park, Seung Hwan; Park, Cheong Sool; Baek, Jun-Geol.

In: International Journal of Industrial Engineering : Theory Applications and Practice, Vol. 26, No. 3, 01.01.2019, p. 394-406.

Research output: Contribution to journalArticle

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