Acceptance sampling plans provide the vendor and the buyer decision rules for lot sentencing to meet their product quality needs. A problem the quality practitioners have to deal with is the ...determination of the critical acceptance values and inspection sample sizes that provide the desired levels of protection to both vendors and buyers. As today's modern quality improvement philosophy, reduction of variation from the target value is the guiding principle as well as reducing the fraction of defectives. The C
pm
index adopts the concept of product loss, which distinguishes the product quality by setting increased penalty to products deviating from the target. In this paper, a variables sampling plan based on C
pm
index is proposed to handle processes requiring very low parts per million (PPM) fraction of defectives with process loss consideration. We develop an effective method for obtaining the required sample sizes n and the critical acceptance value C
0
by solving simultaneously two nonlinear equations. Based on the designed sampling plan, the practitioners can determine the number of production items to be sampled for inspection and the corresponding critical acceptance value for lot sentencing.
This paper describes how to discuss the uncertainty of diameter tolerance by using interval analysis. Firstly, the interval relationship among reliability, diameter tolerance and process capability ...index (PCI) is obtained. Considering the reasonable PCI range, then reliability range is calculated and compared by using universal gray method and combinational method, respectively. Finally, the ranges of improved tolerance and other uncertain variables are obtained. The results show that the reliability and tolerance ranges obtained by universal gray method are more reasonable. This paper provides a research thought for the uncertainty of diameter tolerance.
Statistical process control (SPC) has been shown to be a suitable tool for medical physicists to monitor quality and keep variability low and within specifications. We report our findings regarding ...ionisation chamber stability in our department when using a radioactive stability check device (RSCD) and we compare them with similar previously published records, including calibration results.
We retrospectively studied the stability of a PPC 40 parallel-plate chamber, and two Farmer chambers (FC65-G and FC65-P) by checking them with dedicated RSCDs. We analysed the data following SPC methodology which includes plotting I-MR control charts, monitoring out-of-control observations, calculating process capability ratios (Cp), and estimating conformance to specifications. We also estimated the Cp and adherence to specifications of previously published data.
The PPC40 chamber hardly went out of the control limits over the whole six-year period assessed. However, Farmer chamber verifications drifted in opposite directions in phase II, and the deviations observed did not agree with their calibration records, which only increased by a maximum of 0.5%. In phase I the most unstable chamber was the FC65-P with a Cp equal to 0.9 at a specification level of ±1%. The PPC40 chamber was stable to within a maximum Cp of 1.3. Several sets of analysed data, including ours and those from other authors, fitted well within these limits: within ±1.9% and ±1.5% for a Cp of 1.5 and 1.33 respectively.
SPC with constant long-term RSCD checking gave us a meaningful plot of the instability of our ionisation chambers. Although a period of two years between calibrations should not be surpassed, in the interim this check can conform to specifications of ±1.5%.
•SPC can meaningfully report instability in the constancy of ionisation chambers.•Surpassing the period of two years between calibrations is not recommended.•In the interim, stability can conform to specifications of ±1.5%, according to SPC.
In this paper, we propose a flow path to evaluate the process capability of an entire product composed of multiple process characteristics. There are six steps in the flow path. Whether process data ...comply with a normal distribution or a non-normal distribution, the flow path can be applied. Based on Cpu, Cpl, and Cpn, the research aims to develop a multi-process capability analysis chart (MPCAC) model to evaluate process capability in a normal distribution. Similarly, the research aims to define non-normal multi-process capability analysis chart (NMPCAC) to evaluate process capability in a non-normal distribution based on Npu, Npl, and Npn.
Purpose
The purpose of this study aims to obtain excellent products, consistent investigation and manufacturing process control which are the preconditions that organizations have to consider. ...Nowadays, manufacturing industry apprise process capability index (Cpi) to evaluate the nature of their things with an expect to enhance quality and also to improve the productivity by cutting down the operating cost. In this paper, process capability analysis was applied during wire electrical discharge machining (WEDM) of titanium grade 6, to study the process performance within specific limits.
Design/methodology/approach
Four machine input parameters, namely, pulse ON time, pulse OFF time, wire feed and wire tension, were chosen for process capability study. Experiments were carried out according to Taguchi’s L27 orthogonal array. The value of Cpi was evaluated for two machining attributes, namely, average surface roughness and material removal rate (MRR). For these two machining qualities, single response optimization was executed to explore the input settings, which could optimize WEDM process ability.
Findings
Optimum parameter settings for average surface roughness and MRR were found to be TON: 115 µs, TOFF: 55 µs, WF: 4 m/min and WT: 6 kg−F and TON: 105 µs, TOFF: 60 µs, WF: 4 m/min and WT: 5 kg−F.
Originality/value
Process capability analysis constantly checks the process quality through the capability index keep in mind the end goal to guarantee that the items made are complying with the particulars, providing data for product plan and process quality enhancement for designer and engineers, giving the support to decrease the cost of item failures.
The index Cpmk combines the merits of the three earlier indices Cp. Cpk, and alerts the user if the process variance increases and/or the process mean deviates from its target value. In practice, ...treat the calculated estimate Cpmk as true value and ignore the effect on asymmetric tolerances may lead to misinterpretation of process capability. Peam et al. Pearn, W. L, Chen, K. S. & Lin, P. C. (1999). On the generalizations of the capability index Cpmk for asymmetric tolerances. Far East Journal of Theoretical Statistics, 3(1), 47-661 introduced a generalization of Cpmk, which referred to as to handle processes with asymmetric tolerances. However, the sampling distribution of the estimator Cpmk is exceedingly complex and the derivation of an interval estimation of Cpmk is mathematically intractable. In this paper, we reformulate the explicit formulas and propose a heuristic algorithm to compute a lower confidence bound on Cpmk, which presents a measure on the minimum capability of the process, to enhance the applicability of the theoretical results. Tables are provided to assist the practitioners for a wide range of real world situation involving processes capability analysis. Equations and tables to estimate approximate sample size necessary to achieve a desired confidence limit with a specified confidence level are also developed. An application example on the trench capacitor etch process is also presented for illustrating the applicability of the generalization.
A modified multivariate process capability vector Ganji, Z. Abbasi; Gildeh, B. Sadeghpour
International journal of advanced manufacturing technology,
03/2016, Letnik:
83, Številka:
5-8
Journal Article
Recenzirano
Process capability indices have been widely used in industries to assess the performance of the manufacturing processes. Various different multivariate capability indices have been introduced. In ...this paper, a new multivariate capability vector is proposed under the assumption of multivariate normality, to assess the production capability of the processes that involve multiple product quality characteristics. Also, we investigate the relation between this index and process centering, as well as the relation between this index and the lower and upper bounds of percentage of non-conforming items manufactured. Two real manufacturing data set are used to demonstrate the effectiveness of the proposed index.
Control chart could effectively reflect whether a manufacturing process is currently under control or not. The calculation of control limits of the control chart has been focusing on traditional ...frequency approach, which requires a large sample size for an accurate estimation. A conjugate Bayesian approach is introduced to correct the calculation error of control limits with traditional frequency approach in multi-batch and low volume production. Bartlett's test, analysis of variance test and standardisation treatment are used to construct a proper prior distribution in order to calculate the Bayes estimators of process distribution parameters for the control limits. The case study indicates that this conjugate Bayesian approach presents better performance than the traditional frequency approach when the sample size is small.
It has been proved that process capability indices provide very efficient measures of the capability of processes from many different perspectives. At the present time, the
C
pk
index is used more ...than any other index for measuring process capability. However, most existing research works for capability testing have focused on processes with symmetric tolerances, but not for asymmetric tolerances. 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, which provides the sample sizes necessary to achieve a desired lower confidence bound, is directly related to the cost of the data collection plan. This paper provides explicit formulas with efficient algorithms to obtain the lower confidence bounds and sample sizes required for specified precision of the estimation on
C
pk
for processes with asymmetric tolerances. A
Matlab computer program using a binary search method is developed. For the practitioners to use in their in-plant applications, we tabulate lower confidence bounds for some commonly used capability requirement and the sampling accuracy of
C
pk
for sample sizes determination. A realistic example of forging process is presented to illustrate the applicability of the proposed method.
Capability measure for processes yield with single characteristic has been investigated extensively, but is still comparatively neglected for processes with multiple characteristics. Wu and Pearn Wu, ...C.W., Pearn, W.L., 2005. Measuring manufacturing capability for couplers and wavelength division multiplexers (WDM). International Journal of Advanced Manufacturing Technology 25(5/6), 533–541 proposed a capability index for multiple characteristics called
C
PU
T
, which provides an exact measure on process yield for multiple characteristics with each characteristic normally distributed. However, the exact sampling distribution of
C
PU
T
(multiple characteristics) is analytically intractable. In this paper, we apply the bootstrap method for calculating the lower confidence bounds of the index
C
PU
T
, and determine the sample size for a specified estimation accuracy. In order to obtain a desired estimation quality assurance, the sample size determination is essential as it provides the accuracy of the lower bound obtained from the bootstrap method. For convenience of applications, we tabulate the sample size required for various designated accuracy for the engineers/practitioners to use. A real-world example from manufacturing process with multiple characteristics is investigated to illustrate the applicability of the proposed approach.
