Development of the yield-based process capability index, C py , to flexibly and accurately measure conformance

2016 ◽  
Vol 33 (7) ◽  
pp. 882-899
Author(s):  
George N Kenyon ◽  
R. Samual Sale ◽  
Kurt Hozak ◽  
Paul Chiou
Keyword(s):  
Process Capability ◽  
Process Capability Index ◽  
Process Data ◽  
Process Variability ◽  
Content Type ◽  
Attribute Data ◽  
Capability Index ◽  
Sigma Level ◽  
Multiple Characteristics ◽  
Improved Accuracy

Purpose – The purpose of this paper is to develop an yield-based process capability index (PCI), C py , to overcome the shortcomings of existing PCIs that limit their use and lead to inaccurate measures of quality conformance under a variety of common conditions. Design/methodology/approach –C py is developed conceptually to flexibly and accurately reflect conformance and then used to numerically measure inaccuracies of C pk . Findings –C py overcomes many of the problems associated with existing PCIs, including C pk . The degree of process distribution non-normality, level of quality (the sigma level), and whether the process is centered or shifted left or right affect the direction and size of process capability error produced by C pk . The accuracy of C pk can be greatly affected by process data that deviate even slightly from normality. Practical implications –C py offers numerous advantages compared to existing PCIs. It accurately reflects process conformance regardless of the process distribution. It is applicable even if the process has multiple characteristics and with both variable and attribute data. Its calculation is relatively simple and the necessary data for it are likely already captured by most organizations. Originality/value – The main contributions are the development of a new PCI, C py ; a conceptual analysis of its advantages; and a numerical analysis of the improved accuracy of C py as compared to C pk for shifted and non-shifted process means for normal, nearly normal, and highly non-normal distributions over a range of process variability levels.

Some new methods for comparing the capability of two processes using process capability index Cpm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michael Perakis
Keyword(s):  
Confidence Intervals ◽  
Practical Implication ◽  
Simulated Data ◽  
Process Capability ◽  
Process Capability Index ◽  
Small Samples ◽  
Content Type ◽  
Capability Index ◽  
New Methods ◽  
Practical Implications

PurposeThe purpose of the paper is the construction of confidence intervals for the ratio of the values of process capability index Cpm for two processes. These confidence intervals can be used for comparing the capability of any pair of competitive processes.Design/methodology/approachTwo methods for constructing confidence intervals for the ratio of the values of process capability index Cpm for two processes are proposed. The suggested techniques are based on a two-step approximation of the doubly non-central F distribution. Their performance is tested via simulation.FindingsThe performance of the suggested techniques seems to be rather satisfactory even for small samples, as illustrated through the use of simulated data.Practical implicationsThe practical implication of the suggested techniques is that they can be implemented in real-world applications, since they can be used for comparing the capability of any pair of competitive processes.Originality/valueThe paper presents two new methods for constructing confidence intervals for the ratio of the values of process capability index Cpm for two processes.

A new mixed repetitive group sampling plan based on the process capability index for product acceptance

2018 ◽  
Vol 35 (2) ◽  
pp. 463-480 ◽  
Author(s):  
Balamurali Saminathan ◽  
Usha Mahalingam
Keyword(s):  
Process Capability ◽  
Process Capability Index ◽  
Sampling Plan ◽  
Optimal Parameters ◽  
Sampling Plans ◽  
Content Type ◽  
Practical Applications ◽  
Capability Index ◽  
Unknown Variance ◽  
Repetitive Group Sampling

Purpose The purpose of this paper is to propose a new mixed repetitive group sampling (RGS) plan based on the process capability index, Cpk, where the quality characteristics of interest follow the normal distribution with unknown mean and unknown variance. Tables are constructed to determine the optimal parameters for practical applications for both symmetric and asymmetric fraction non-conforming cases. The advantages of this proposed mixed sampling plan are also discussed. The proposed sampling plan is also compared with other existing sampling plans. Design/methodology/approach In order to determine the optimal parameters of the proposed mixed RGS plan based on Cpk, the authors constructed tables for various combinations of acceptable and limiting quality levels (LQLs). For constructing tables, the authors followed the approach of two points on the operating characteristic (OC) curve. The optimal problem is formulated as a non-linear programming where the objective function to be minimized is the average sample number (ASN) and the constraints are related to lot acceptance probabilities at acceptable quality level and LQL under the OC curve. Findings The proposed mixed RGS plan will be a new addition to the literature of acceptance sampling. It is shown that the proposed mixed plan involves minimum ASN with desired protection to both producers and consumers compared to other existing sampling plans. The practical application of the proposed mixed sampling plan is also explained with an illustrative real-time example. Originality/value In this paper, the authors propose a new mixed RGS plan based on the process capability index Cpk, where the quality characteristic of interest follows the normal distribution with unknown mean and unknown variance. Tables are constructed to determine the optimal parameters for practical applications. The proposed mixed sampling plan can be used in all production industries. This kind of mixed RGS plan is not available in the literature.

Performance analysis in WEDM of titanium grade 6 through process capability index

2020 ◽  
Vol 17 (1) ◽  
pp. 144-151
Author(s):  
Himadri Majumder ◽  
Kalipada Maity
Keyword(s):  
Surface Roughness ◽  
Process Capability ◽  
Process Capability Index ◽  
Average Surface Roughness ◽  
Content Type ◽  
Average Surface ◽  
Process Capability Analysis ◽  
Capability Index ◽  
Capability Analysis ◽  
Titanium Grade

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.

New multivariate process capability indices for measuring the performance of multivariate processes subject to non-normal distributions

2015 ◽  
Vol 33 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Jeh-Nan Pan ◽  
Chung-I Li ◽  
Wei-Chen Shih
Keyword(s):  
Process Capability ◽  
Process Capability Indices ◽  
Process Data ◽  
Multivariate Process ◽  
Multivariate Processes ◽  
Content Type ◽  
Normal Distributions ◽  
Normality Assumption ◽  
Capability Indices ◽  
Multivariate Manufacturing

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC p , RNMC pm and RNMC pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC p and NMC pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.

A mixed control chart using process capability index

2017 ◽  
Vol 36 (2) ◽  
pp. 278-289 ◽  
Author(s):  
Muhammad Aslam ◽  
N. Khan ◽  
Liaquat Ahmad ◽  
Chi-Hyuck Jun ◽  
Jaffer Hussain
Keyword(s):  
Control Chart ◽  
Process Capability ◽  
Process Capability Index ◽  
Capability Index ◽  
Mixed Control
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