Browsing by Author "Shu, MH"

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An algorithm for calculating the lower confidence bounds of C-PU and C-PL with application to low-drop-out linear regulators
(2003-03-01)
Pearn, WL
;
Shu, MH
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
In assessing the performance of normal stable manufacturing processes with one-sided specification limits, process capability indices C-PU and C-PL have been widely used to measure the process capability. The purpose of this paper is to develop an algorithm to compute the lower confidence bounds on C-PU and C-PL using the UMVUEs of C-PU and C-PL. The lower confidence bound presents a measure on the minimum capability of the process based on the sample data. We also provide tables for the engineers/practitioners to use in measuring their processes. A real-world example taken from a microelectronics device manufacturing process is investigated to illustrate the applicability of the algorithm. Implementation of the existing statistical theory for capability assessment fills the gap between the theoretical development and the in-plant applications. (C) 2003 Elsevier Science Ltd. All rights reserved.
An algorithm for calculating the lower confidence bounds of C-PU and C-PL with application to low-drop-out linear regulators (vol 43, pg 495, 2003)
(2003-08-01)
Pearn, WL
;
Shu, MH
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
C-pm MPPAC for manufacturing quality control applied to precision voltage reference process
(2004-05-01)
Pearn, WL
;
Shu, MH
;
Hsu, BM
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
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.
C-pm MPPAC for manufacturing quality control applied to the precision voltage reference process
(2004-07-01)
Pearn, WL
;
Shu, MH
;
Hsu, BM
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The multiprocess performance analysis chart (MPPAC) based on the process capability index C-pm, called C-pm MPPAC, is developed for analysing the manufacturing quality of a group of processes in a multiple process environment. The C-pm MPPAC conveys critical information of multiple processes regarding the departure of the process and process variability from one single chart. Existing research works on MPPAC are restricted to obtaining quality information from one single sample of each process ignoring sampling errors. The information provided from existing MPPAC charts, therefore, is unreliable and misleading and results in incorrect decisions. In this paper, we consider the natural estimator of C-pm based on multiple samples. Based on the natural estimator of C-pm, we consider the sampling errors by providing an explicit formula with the Matlab program to obtain the estimation accuracy of the C-pm. We tabulate the sampling accuracy of C-pm for sample size determination so that the engineers/practitioners can use it for their in-plant applications. An example of multiple precision voltage reference (PVR) processes is presented to illustrate the applicability of C-pm MPPAC for manufacturing quality control.
Lower confidence bounds for C-PU and C-PL based on multiple samples with application to production yield assurance
(2004-06-15)
Pearn, WL
;
Shu, MH
;
Hsu, BM
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
For stably normal processes with one-sided specification limits, capability indices C-PU and C-PL have been used to provide numerical measures on production yield assurance. Statistical properties of the estimators of C-PU and C-PL have been investigated extensively for cases with a single sample. It is shown that for multiple samples, the uniformly minimum-variance unbiased estimators of C-PU and C-PL are consistent and asymptotically efficient. Based on the uniformly minimum-variance unbiased estimators, an algorithm is developed with an efficient program using a direct search method to compute the lower confidence bounds for C-PU and C-PL. The lower confidence bounds convey critical information to the minimum capability of a process, providing a necessary yield assurance of production. The lower confidence bounds are tabulated for some commonly used capability requirement so that engineers/practitioners can use them for their in-plant applications. An example of a high-speed buffer amplifier is presented to illustrate the practicality of the approach to data collected from the factories for production yield assurance.
Lower confidence bounds with sample size information for C-pm applied to production yield assurance
(2003-10-15)
Pearn, WL
;
Shu, MH
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The process capability index C-pm, sometimes called the Taguchi index, has been proposed to the manufacturing industry as providing numerical measures on process performance. A lower confidence bound estimates the minimum process capability, conveying critical information regarding product quality, which is essential to quality assurance. The sample size determination is directly related to the cost of the data collection plan. The purpose of this paper is to provide explicit formulas with efficient algorithms to obtain the lower confidence bounds and sample sizes required for specified precision of the estimation on C-pm using the maximum likelihood estimator (MLE) of C-pm. We also provide tables for the engineers/practitioners to use for their in-plant applications. A real-world example taken from a microelectronics manufacturing process is investigated to illustrate the applicability of the proposed approach. The implementation of existing statistical theory for capability assessment bridges the gap between the theoretical development and factory applications.
Manufacturing capability control for multiple power-distribution switch processes based on modified C-pk MPPAC
(2003-06-01)
Pearn, WL
;
Shu, MH
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
A modification of the multiple process performance analysis chart based on process capability index C-pk, called modified C-pk MPPAC, has been developed for controlling product quality/reliability of a group of multiple manufacturing processes. The modified C-pk MPPAC conveys critical information of each individual process regarding process accuracy and process precision from one single chart, which is an effective tool for controlling product quality/reliability for multiple processes. Existing MPPAC charts never considered sampling errors hence the capability information provided from those charts is often unreliable and misleading. In this paper, we develop an efficient algorithm to compute the lower confidence bounds of C-pk. The lower confidence bound presents the minimum true capability of the process, which is essential to product reliability assurance. We apply the lower confidence bounds to the modified C-pk MPPAC to provide reliable simultaneous capability control for multiple processes. A case involving multiple processes manufacturing power-distribution switch (PDS) is investigated. The modified C-pk MPPAC incorporating with the lower confidence bound is applied to the capability control of multiple PDS processes. (C) 2003 Elsevier Science Ltd. All rights reserved.
Measuring manufacturing capability based on lower confidence bounds of C-pmk applied to current transmitter process
(2004-01-01)
Pearn, WL
;
Shu, MH
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
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.
Monitoring manufacturing quality for multiple Li-BPIC processes based on capability index C-pmk
(2005-06-15)
Pearn, WL
;
Shu, MH
;
Hsu, BM
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The multi-process performance analysis chart (MPPAC) based on process capability indices has been developed to analyse the manufacturing performance for multiple processes, which conveys critical information regarding the departure of the process mean from the target value, process variability, capability levels, which provides a guideline of directions for capability improvement. Existing MPPAC researches have plotted the sample estimates of the process indices on the chart. Conclusions were then made on whether processes meet the capability requirement and directions need to be taken for further quality improvement. Such an approach is highly unreliable since the sample point estimate is a random variable with no assessment of the sampling errors. Further, existing MPPAC researches only considered one single sample. Current quality control practice is to estimate process capability using multiple groups of control chart samples rather than one single sample. In this paper, we propose the C-pmk MPPAC combining the accuracy index C-a to access the performance of multiple manufacturing processes. Distributions of the estimated C-pmk and C-a are derived based on multiple control chart samples, and accurate lower confidence bounds are calculated. The lower confidence bounds of the estimated Cpmk and Ca are then employed to the MPPAC to provide reliable capability grouping for those multiple processes. A real-world example is presented to illustrate the applicability of the proposed MPPAC.
Testing process capability based on C-pm in the presence of random measurement errors
(2005-12-01)
Pearn, WL
;
Shu, MH
;
Hsu, BM
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Process capability indices have been widely used in the manufacturing industry providing numerical measures on process performance. The index C-p provides measures on process precision (or product consistency). The index C-pm,C- sometimes called the Taguchi index, meditates on process centring ability and process loss. Most research work related to C-p and C-pm assumes no gauge measurement errors. This assumption insufficiently reflects real situations even with highly advanced measuring instruments. Conclusions drawn from process capability analysis are therefore unreliable and misleading. In this paper, we conduct sensitivity investigation on process capability C-p and C-pm in the presence of gauge measurement errors. Due to the randomness of variations in the data, we consider capability testing for C-p and C-pm to obtain lower confidence bounds and critical values for true process capability when gauge measurement errors are unavoidable. The results show that the estimator with sample data contaminated by the measurement errors severely underestimates the true capability, resulting in imperceptible smaller test power. To obtain the true process capability, adjusted confidence bounds and critical values are presented to practitioners for their factory applications.