Process capability monitoring chart with an application in the silicon-filler manufacturing process

Chen, Kuen‐Suan; Yu, Kun-Tzu; Sheu, Shey‐Huei

doi:10.1016/j.ijpe.2005.11.004

Cited by 22 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various applications of PCI in manufacturing industry were considered by Pearn et al . and Chen et al . More recent researches on PCI include Pearn et al ., Awad and Kovach, Wu et al ., and Yum and Kim …”

Section: The Generalization C″pk Indexmentioning

confidence: 99%

Measuring PPM Non‐conformities for Processes with Asymmetric Tolerances

Pearn

2012

Quality & Reliability Eng

View full text Add to dashboard Cite

Process yield has been the most basic and common criterion used in the manufacturing industry for evaluating process capability. The C pk index has been used widely in the manufacturing industry. In this note, we considered a generalization of C pk index which handles processes involving a target T with asymmetric tolerances. Particularly, we established a formula for measuring the PPM non-conformities for given ratios of the two-side tolerances. We proved the validity of the established formula and tabulated the upper bounds on PPM non-conformities for various given C pk index values and ratios of the two-side tolerances.where d* = min{D L , D U }, D U = USL À T, D L = T À LSL and T is the target value. Obviously, for processes with symmetric tolerances (D U = D L ), C 00 pk reduces to the C pk index which mentioned earlier in equation (1). Pearn and Lin 2 investigated the statistical estimation of C 00 pk index. Comparisons among several process capability indices (PCI) for processes with asymmetric tolerances were proposed by Chen and Pearn. 3 Lin and Pearn 4 analyzed the large sample properties of the natural estimator of C 00 pk under general condition and provided an approximate confidence interval using the limiting distribution.

show abstract

Section: The Generalization C″pk Indexmentioning

confidence: 99%

Measuring PPM Non‐conformities for Processes with Asymmetric Tolerances

Pearn

2012

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…The Process Capability Monitoring Chart (PCMC) interprets multi-characteristics process capabilities and hence is a useful tool to evaluate the manufacturing process performance [4]. PCMC is an effective and efficient tool for examining the process capabilities of a product of multiple processes.…”

Section: Pcmc (Process Capability Monitoring Chart)mentioning

confidence: 99%

Quality Assurance through Process Improvement - A Concise Review

Sharma¹,

Rao²,

Babu

2016

View full text Add to dashboard Cite

Abstract. The past two decades have seen the realization of the manufacturing firms towards quality consciousness. The various firms worldwide employed the statistical tools for minimizing the deviations and subsequently the number of rejects of the manufactured parts. In this process, more prominence is laid on prevention of defects rather than detecting and rejecting the defect in the usual traditional end inspection quality check. This review paper witnesses the procedures involved in the improvement of the manufacturing process capability and focuses on achieving quality assurance through sustained improvement in the manufacturing process. The different methodologies for sustained improvement in manufacturing process across varied industrial sectors, are identified, discussed and presented. The nuances of enhanced product quality levels through process capability improvement work performed in varied areas of industrial manufacturing are also captured.

show abstract

“…This example involves a polymerization process in a silicon‐filler manufacturing factory36. The density x of the silicon filler is a key quality characteristic.…”

Section: An Examplementioning

confidence: 99%

A comparison study on effectiveness and robustness of control charts for monitoring process mean and variance

et al. 2011

Quality & Reliability Eng

View full text Add to dashboard Cite

This article compares the effectiveness and robustness of nine typical control charts for monitoring both process mean and variance, including the most effective optimal and adaptive sequential probability ratio test (SPRT) charts. The nine charts are categorized into three types (theX type, CUSUM type and SPRT type) and three versions (the basic version, optimal version and adaptive version). While the charting parameters of the basic charts are determined by common wisdoms, the parameters of the optimal and adaptive charts are designed optimally in order to minimize an index average extra quadratic loss for the best overall performance. Moreover, the probability distributions of the mean shift and standard deviation shift are studied explicitly as the influential factors in a factorial experiment. The main findings obtained in this study include: (1) From an overall viewpoint, the SPRT-type chart is more effective than the CUSUM-type chart andX-type chart by 15 and 73%, respectively; (2) in general, the adaptive chart outperforms the optimal chart and basic chart by 16 and 97%, respectively; (3) the optimal CUSUM chart is the most effective fixed sample size and sampling interval chart and the optimal SPRT chart is the best choice among the adaptive charts; and (4) the optimal sample sizes of both theX charts and the CUSUM charts are always equal to one. Furthermore, this article provides several design tables which contain the optimal parameter values and performance indices of 54 charts under different specifications.

show abstract

Process capability monitoring chart with an application in the silicon-filler manufacturing process

Cited by 22 publications

References 12 publications

Measuring PPM Non‐conformities for Processes with Asymmetric Tolerances

Measuring PPM Non‐conformities for Processes with Asymmetric Tolerances

Quality Assurance through Process Improvement - A Concise Review

A comparison study on effectiveness and robustness of control charts for monitoring process mean and variance

Contact Info

Product

Resources

About