Study of artificial neural network scheme application in manufacturing industry for monitoring-diagnosis bivariate process variation
In manufacturing industries, process variation is known to be a major source of poor quality. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two or more correlated variables (multivariate). Process moni...
Saved in:
主要作者: | Majid, Mariam |
---|---|
格式: | Thesis |
語言: | English English English |
出版: |
2014
|
主題: | |
在線閱讀: | http://eprints.uthm.edu.my/1531/1/24p%20MARIAM%20MAJID.pdf http://eprints.uthm.edu.my/1531/2/MARIAM%20MAJID%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1531/3/MARIAM%20MAJID%20WATERMARK.pdf http://eprints.uthm.edu.my/1531/ |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
相似書籍
-
Synergistic artificial neural network scheme for monitoring and diagnosis of multivariate process variation in mean shifts
由: Marian, Mohd Fairuz
出版: (2014) -
A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
由: Masood, Ibrahim
出版: (2012) -
Diagnosis of bivariate process variation using an integrated mspc-ann scheme
由: Masood, Ibrahim, et al.
出版: (2016) -
Design optimization for the two-stage bivariate pattern recognition scheme
由: Mokhtar, Mohd Shukri
出版: (2015) -
Identification of shift variation in bivariate process using pattern recognition technique
由: Mohd Haizan, Mohamad Azrul Azhad
出版: (2019)