Diagnosis of bivariate process variation using an integrated mspc-ann scheme

• Main Authors: Masood, Ibrahim, Ali, Rasheed Majeed, Mohd Solihin, Nurul Adlihisam, Elewe, Adel Muhsin
• Format: Article
• Language: English
• Published: ARPN Journal 2016
• Subjects: TS155-194 Production management. Operations management
• Online Access: http://eprints.uthm.edu.my/3819/1/AJ%202016%20%286%29.pdf; http://eprints.uthm.edu.my/3819/; http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0616_4518.pdf
• Institution: Universiti Tun Hussein Onn Malaysia

Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. This is referred to as imbalanced performance monitoring. The problem becomes more complicated when dealing with small mean shift particularly in identifying the causable variables. In this research, a scheme that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN models, and monitoring-diagnosis approach based on single stage and two stages techniques. The study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 ̴ 7.78 ( for out-of-control) and ARL0 = 4λ1.03 (small shifts) and 524.80 (large shifts) in control process and the grand average for recognition accuracy (RA) = λ6.36 ̴ λ8.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts.