Abstract
Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modern manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description-based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing.
| Original language | English |
|---|---|
| Pages (from-to) | 18-31 |
| Number of pages | 14 |
| Journal | Engineering Applications of Artificial Intelligence |
| Volume | 68 |
| DOIs | |
| Publication status | Published - 2018 Feb 1 |
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Keywords
- Machine learning
- Multivariate control chart
- Nonstationary process
- Process control
- Support vector data description
- Time-varying process
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
Cite this
Time-adaptive support vector data description for nonstationary process monitoring. / Lee, Seulki; Kim, Seoung Bum.
In: Engineering Applications of Artificial Intelligence, Vol. 68, 01.02.2018, p. 18-31.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Time-adaptive support vector data description for nonstationary process monitoring
AU - Lee, Seulki
AU - Kim, Seoung Bum
PY - 2018/2/1
Y1 - 2018/2/1
N2 - Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modern manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description-based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing.
AB - Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modern manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description-based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing.
KW - Machine learning
KW - Multivariate control chart
KW - Nonstationary process
KW - Process control
KW - Support vector data description
KW - Time-varying process
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U2 - 10.1016/j.engappai.2017.10.016
DO - 10.1016/j.engappai.2017.10.016
M3 - Article
AN - SCOPUS:85033363759
VL - 68
SP - 18
EP - 31
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
SN - 0952-1976
ER -