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 language | English |
|---|---|
| Pages (from-to) | 394-406 |
| Number of pages | 13 |
| Journal | International Journal of Industrial Engineering : Theory Applications and Practice |
| Volume | 26 |
| Issue number | 3 |
| Publication status | Published - 2019 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C2005949). This work was supported by BK21 Plus program (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd. Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1G1A1004084).
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C2005949). This work was supported by BK21 Plus program (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd. Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF- 2019R1G1A1004084).
Publisher Copyright:
© 2019 University of Cincinnati. All rights reserved.
Keywords
- Manufacturing process simulation
- Multi-profile
- Prediction model
- Profile integrated measure
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering