C pm MPPAC for manufacturing quality control applied to precision voltage reference process

A multiprocess performance analysis chart (MPPAC), based on the process capability index C _pm , called C _pm MPPAC, is developed to analyse the manufacturing quality of a group of processes in a multiple process environment. The C _pm MPPAC conveys critical information about multiple processes regarding the departure of the process and process variability on one single chart. Existing research on MPPAC has been restricted to obtaining quality information from one single sample of each process, ignoring sampling errors. The information provided from the existing MPPAC chart, therefore, is unreliable and misleading, resulting in incorrect decisions. In this paper, the natural estimator of C _pm is considered based on multiple samples. Based on the natural estimator of C _pm , sampling errors are considered by providing an explicit formula with Matlab to obtain the estimation accuracy of the C _pm . The sampling accuracy of C _pm is tablulated for sample size determination so that engineers/practitioners can use it for in-plant applications. An example of multiple PVR processes is presented to illustrate the applicability of C _pm MPPAC for manufacturing quality control.     

Measuring manufacturing capability based on lower confidence bounds of C pmk applied to current transmitter process

Several process capability indices, including C_p, C_pk, and C_pm, have been proposed to provide numerical measures on manufacturing potential and actual performance. Combining the advantages of those indices, a more advanced index C_pmk is proposed, taking the process variation, centre of the specification tolerance, and the proximity to the target value into account, which has been shown to be a useful capability index for manufacturing processes with two-sided specification limits. In this paper, we consider the estimation of C_pmk, and we develop an efficient algorithm to compute the lower confidence bounds on C_pmk based on the estimation, which presents a measure on the minimum manufacturing capability of the process based on the sample data. We also provide tables for practitioners to use in measuring their processes. A real-world example of current transmitters taken from a microelectronics device manufacturing process is investigated to illustrate the applicability of the proposed approach. Our implementation of the existing statistical theory for manufacturing capability assessment bridges the gap between the theoretical development and the in-plant applications.