Measuring the Capability for One Process with Spherical Tolerance

2013 ◽  
Vol 284-287 ◽  
pp. 3717-3726
Author(s):  
Liang Chyau Sheu ◽  
Chi Huang Yeh ◽  
Ching Ho Yen ◽  
Chia Hao Chang
Keyword(s):  
Manufacturing Industry ◽  
Process Capability ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Measuring Process ◽  
Process Yield ◽  
Effective Measure ◽  
Capability Indices ◽  
Process Loss ◽  
Loss Index

Process capability indices, Cp, Cpk, and Cpm, are well-known indices used widely in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications, but limited to cases with single engineering specification. Therefore, for processes where the quality characteristic is the location relative to a specific location, they can not provide an effective measure. In this paper, we propose a process loss index LG to evaluate the process capability for this issue. Based on the index, we provide the corresponding transformation for production yield. In addition, we tabulate some critical values for process loss index LG to judge if the process capability is capable. The proposed method is useful for the practitioners to measure the process loss and determine whether a process meets the present process yield requirement.

scholarly journals Performance Evaluation Using Multivariate Non-Normal Process Capability

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 833 ◽  
Author(s):  
Alatefi ◽  
Ahmad ◽  
Alkahtani
Keyword(s):  
Manufacturing Industry ◽  
Process Capability ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Manufacturing Processes ◽  
Capability Indices ◽  
Skewness Reduction ◽  
Quality Of Products

Process capability indices (PCIs) have always been used to improve the quality of products and services. Traditional PCIs are based on the assumption that the data obtained from the quality characteristic (QC) under consideration are normally distributed. However, most data on manufacturing processes violate this assumption. Furthermore, the products and services of the manufacturing industry usually have more than one QC; these QCs are functionally correlated and, thus, should be evaluated together to evaluate the overall quality of a product. This study investigates and extends the existing multivariate non-normal PCIs. First, a multivariate non-normal PCI model from the literature is modeled and validated. An algorithm to generate non-normal multivariate data with the desired correlations is also modeled. Then, this model is extended using two different approaches that depend on the well-known Box–Cox and Johnson transformations. The skewness reduction is further improved by applying heuristics algorithms. These two approaches outperform the investigated model from the literature because they can provide more precise results regardless of the skewness type. The comparison is made based on the generated data and a case study from the literature.

UNIVARIATE PROCESS CAPABILITY INDICES FOR UNILATERAL SPECIFICATION REGION — A REVIEW AND SOME MODIFICATIONS

2012 ◽  
Vol 19 (04) ◽  
pp. 1250020 ◽  
Author(s):  
MOUTUSHI CHATTERJEE ◽  
ASHIS KUMAR CHAKRABORTY
Keyword(s):  
Manufacturing Industry ◽  
Process Capability ◽  
Extensive Study ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Practical Application ◽  
Specification Limit ◽  
Capability Indices ◽  
Lower Specification Limit ◽  
Upper Specification Limit

In a manufacturing industry, often the quality characteristic under study is found to have only one of the two specification limits viz., upper specification limit (USL) or lower specification limit (LSL). In such cases the process capability indices (PCI) designed for bilateral specifications become inappropriate. However, in the literature only a few indices are available to address this problem. In the present paper, we have made an extensive study of the PCI's for unilateral specifications with a brief discussion of their possible fields of applications and drawbacks, if any. We have also proposed a logical formulation of a parameter of one of these indices which reduces the subjectivity of the index and hence makes it more suitable for practical application. An example, with the computed values of the various PCI's, is discussed to make a comparative study of the performance and inter-relationship between these PCI's. We have concluded the paper with a discussion on the future scope of study in this field.

scholarly journals Bayesian Estimation of Generalized Process Capability Indices

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Sudhansu S. Maiti ◽  
Mahendra Saha
Keyword(s):  
Bayesian Estimation ◽  
Process Capability ◽  
Life Time ◽  
Quality Characteristic ◽  
Process Capability Indices ◽  
Data Sets ◽  
Squared Error Loss Function ◽  
Specification Limit ◽  
Capability Indices ◽  
Lower Specification Limit

Process capability indices (PCIs) aim to quantify the capability of a process of quality characteristic (X) to meet some specifications that are related to a measurable characteristic of its produced items. One such quality characteristic is life time of items. The specifications are determined through the lower specification limit (L), the upper specification limit (U), and the target value (T). Maiti et al. (2010) have proposed a generalized process capability index that is the ratio of proportion of specification conformance to proportion of desired conformance. Bayesian estimation of the index has been considered under squared error loss function. Normal, exponential (nonnormal), and Poisson (discrete) processes have been taken into account. Bayes estimates of the index have been compared with the frequentist counterparts. Data sets have been analyzed.

scholarly journals Concept of process capability indices as a tool for process performance measures and its pharmaceutical application

2020 ◽  
Vol 10 (5) ◽  
pp. 333-344
Author(s):  
Abikesh Prasada Kumar Mahapatra ◽  
Jianwu Song ◽  
Zhibo Shao ◽  
Tang Dong ◽  
Zihong Gong ◽  
...  
Keyword(s):  
Control Charts ◽  
Manufacturing Industry ◽  
Drug Product ◽  
Control Process ◽  
Process Capability ◽  
Process Capability Indices ◽  
Process Performance ◽  
Process Robustness ◽  
Capability Indices ◽  
Pharmaceutical Industries

The main objective of the present study is to present the concept of process capability and to focus its significance in pharmaceutical industries. From a practical view point, the control charts (such as X and R hart) sometimes are not convenient summary statistics when hundreds of characteristics in a plant or supply base are considered. In many situations, capability indices can be used to relate the process parameters. The resulting indices are unit less and provide a common, easily understood language for quantifying the performance of a process. Process capability indices (PCIs) are powerful means of studying the process ability for manufacturing a product that meets specifications. Several capability indices including Cp, Cpu, Cpl and Cpk have been widely used in manufacturing industry to provide common quantitative measures on process potential and performance. The formulas for these indices are easily understood and can be directly implemented. A process capability analysis compares the distribution of output from an in-control process to its specifications limits to determine the consistency with which the specifications can be met. The process capability is also having a significant role in pharmaceutical industry. Process capability indices can be a powerful tool by which to ensure drug product quality and process robustness. Determining process capability provides far more insight into any pharmaceutical process performance than simply computing the percentage of batches that pass or fail each year. Keywords: Process capability; Cp/Cpk; Pp/Ppk; Pharmaceutical quality, process robustness, specification

scholarly journals Measuring process capability index C pmk with fuzzy data and compare it with other fuzzy process capability indices

2011 ◽  
Vol 38 (6) ◽  
pp. 6452-6457 ◽  
Author(s):  
Mohammad Abdolshah ◽  
Rosnah Mohd. Yusuff ◽  
Tang Sai Hong ◽  
Md. Yusof B. Ismail ◽  
Aghdas Naimi Sadigh
Keyword(s):  
Process Capability ◽  
Process Capability Index ◽  
Process Capability Indices ◽  
Fuzzy Data ◽  
Measuring Process ◽  
Capability Index ◽  
Fuzzy Process ◽  
Capability Indices ◽  
Fuzzy Process Capability

scholarly journals Process Quality Evaluation Model with Taguchi Cost Loss Index

2021 ◽  
Vol 11 (21) ◽  
pp. 10182
Author(s):  
Chiao-Tzu Huang ◽  
Kuei-Kuei Lai
Keyword(s):  
Confidence Interval ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Process Capability ◽  
Process Quality ◽  
Statistical Testing ◽  
Process Capability Indices ◽  
Capability Indices ◽  
Loss Index ◽  
Quality Evaluation Model

Process Capability Indices (PCIs) are not only a good communication tools between sales departments and customers but also convenient tools for internal engineers to evaluate and analyze process capabilities of products. Many statisticians and process engineers are dedicated to research on process capability indices, among which the Taguchi cost loss index can reflect both the process yield and process cost loss at the same time. Therefore, in this study the Taguchi cost loss index was used to propose a novel process quality evaluation model. After the process was stabilized, a process capability evaluation was carried out. This study used Boole’s inequality and DeMorgan’s theorem to derive the (1 – α) ×100% confidence region of (δ,γ2) based on control chart data. The study adopted the mathematical programming method to find the (1 – α) ×100% confidence interval of the Taguchi cost loss index then employed a (1 – α) ×100% confidence interval to perform statistical testing and to determine whether the process needed improvement.

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