In today's competitive manufacturing environment, the challenge is to responsively produce products with minimum cost and high quality. Achieving and controlling the targeted quality level in manufacturing processes does not only increase customer satisfaction, but it can also result in significant cost and time savings. Further, measuring the process performance is a critical issue in process improvement initiatives. The common practice in several industries is using the Univariate Process Capability Indices (UPCIs) to measure the process performance, which are based on only a single quality characteristic. In most of the applications, it is not acceptable to judge the performance based on a single quality characteristic as it actually relies on more than one characteristic. In this paper, univariate and multivariate PCIs are used to measure the performance of the flare making process. This process is a critical step in the straight fluorescent light bulb production line. In addition, multivariate control charts such as the Hotelling ܶ ଶ as well as the Multivariate Exponentially Weighted Moving Average (MEWMA) are constructed for the collected data to verify that the process is in control before assessing its capability. Besides, Principal Component Analysis (PCA) and Joint Normal Distribution (JND) techniques are applied in the multivariate process capability assessment. In this paper, Multivariate Process Capability Indices (MPCIs) have been evaluated to compare the process performance before and after improvement efforts. In the considered case study, MPCIs provide the user with an overall assessment of process capability regardless of the fluctuations in the individual variables capabilities.
The complex nature for steelmaking processes makes the classical Statistical Process Control (SPC) methodologies are optimal when used to monitor and control steam boiler generation used to supply the required steam for vacuum degassing processes. These processes include a large number of variables that need to be monitored and controlled, while classical SPC requires a control chart for each variable. Thus the effect of one variable can be confounded with effects of other correlated variables. Such a situation can lead to false alarm signals. Univariate control charts are also difficult to manage and analyze because of the large numbers of control charts of each variable. An alternative approach is to construct a single multivariate control T2 chart that minimizes the occurrence of false process alarms as well as monitors the relationships between the variables, and identifies real process changes not detectable using univariate charts. It is necessary to simultaneously monitor and control these variables to achieve optimal vacuum degassing process performance to remove harmfid gases from the molten steel before casting. This represents the main concern of the presented paper. This paper also studies the application of univariate and multivariate control charts in the field of steel industry. The performance analysis for each one is studied using the Average Run Length (ARL). A comparison of the univariate out-of-control signals based on the multivariate out-of-control signals is also made to illustrate the efficiency of the Hotelling's T' statistics.
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