IMPROVING AUTOMATIC-CONTROLLED PROCESS QUALITY USING ADAPTIVE PRINCIPAL COMPONENT MONITORING

Manufacturing quality is an important factor in global market competition. The implementation of statistical process control (SPC) techniques is necessary if a manufacturing firm desires to be competitive in the global market. With advances in sensing and data capture technologies, large volumes of data are routinely being collected in automatic-controlled processes. There is a great need for SPC techniques for variation reduction and quality improvement in these processes. This paper focuses on SPC schemes that are based on a combination of the process outputs and automatic control actions using adaptive principal component monitoring (APCM). These schemes are more efficient in detecting process changes for automatic-controlled processes.

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