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.

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.

Analyzing processing quality of machine tools via processed product: Example of ball valve processing machine

2020 ◽  
Vol 234 (4) ◽  
pp. 331-341 ◽  
Author(s):  
Kuen-Suan Chen ◽  
Der-Fa Chen ◽  
Ming-Chieh Huang ◽  
Tsang-Chuan Chang
Keyword(s):  
Machine Tools ◽  
Process Capability ◽  
Quality Characteristics ◽  
Process Quality ◽  
Quality Analysis ◽  
Process Capability Indices ◽  
Processing Quality ◽  
Accuracy And Precision ◽  
Capability Indices

Machine tools are fundamental equipment in industrial production, and their processing quality exerts a direct impact on the quality of the component product that they process. Thus, machine tool manufacturers develop various machine tools depending on market needs and processing functions, and the processed component products generally possess multiple smaller-the-better, larger-the-better, and nominal-the-best quality characteristics at the same time. For this reason, this study employed the widely used process capability indices, [Formula: see text], [Formula: see text], and [Formula: see text] to develop a model that can evaluate the process quality of component products and analyze the processing quality of various machine tools. We first converted the process capability indices into functions of the accuracy and precision indices and constructed a multi-characteristic quality analysis chart that can identify the reason for poor process quality in a quality characteristic. Furthermore, considering the fact that the process capability indices can only be estimated, which may lead to misjudgment in the evaluation of process quality, we derived the [Formula: see text] upper confidence limits of indices and the coordinates formed by the corresponding accuracy and precision indices. Manufacturers can then evaluate the process quality levels of the quality characteristics based on where the coordinates falls in the multi-characteristic quality analysis chart. This can more reliably assist manufacturers in monitoring the processing quality of their machine tools and providing feedback to the machine tool manufacturers for machine improvement.

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 METODOLOGÍA PARA LA ESTIMACIÓN DE ÍNDICES DE CAPACIDAD EN PROCESOS PARA DATOS NO NORMALES

2017 ◽  
Vol 24 (1) ◽  
pp. 43
Author(s):  
Carlos W. Camero Jiménez ◽  
Erick . A. Chacón Montalvan ◽  
Vilma S. Romero Romero ◽  
Luisa E. Quispe Ortiz
Keyword(s):  
Distribution Systems ◽  
Process Capability ◽  
Process Capability Indices ◽  
Data Transformations ◽  
Frequency Distributions ◽  
Palabras Clave ◽  
Capability Indices ◽  
Proper Estimation ◽  
Made In

La globalización ha ido intensificando la competencia en muchos mercados. Con el fin de mantener su competitividad, las empresas buscan satisfacer las necesidades de los clientes mediante el cumplimiento de los requerimientos del mercado. En este contexto, los Índices de Capacidad de Proceso (ICP) juegan un rol trascendental en el análisis de capacidad de los procesos. Para el caso de datos no normales existen dos enfoques generales basados en transformaciones (Transformación de Box –Cox y de Johnson) y percentiles (Sistemas de distribuciones de Pearson y de Burr). Sin embargo, estudios anteriores sobre la comparación de tales métodos muestran distintas conclusiones y por ello nace la necesidad de aclarar las diferencias que existen entre estos métodos para poder implementar una correcta estimación de estos índices. En este trabajo, se realiza un estudio de simulación con el objetivo de comparar los métodos mencionados y proponer una metodología adecuada para la estimación del ICP en datos no normales. Además, se concluye que el mejor método a emplear depende del tipo de distribución, el nivel de asimetría de la misma y el valor del ICP. Palabras clave.- Ajuste de distribuciones de frecuencia, Índice de capacidad del proceso, normalidad, Transformación de datos, Simulación. ABSTRACTGlobalization has intensified competition in many markets. To remain competitive, the companies look for satisfying the needs of customers by meeting market requirements. In this context, Process Capability Indices (PCI) play a crucial role in assessing the quality of processes. In the case of non-normal data there are two general approaches based on transformations (Box-Cox and Johnson Transformation) and Percentiles (Pearson’s and Burr’s Distribution Systems). However, previous studies on the comparison of these methods show different conclusions, and thus arises the need to clarify the differences between these methods to implement a proper estimation of these indices. In this paper, a simulation study is made in order to compare the above methods and to propose an appropriate methodology for estimating the PCI in non-normal data. Furthermore, it is concluded that the best method used depends on the type of distribution, the asymmetry level of the distribution and the ICP value. Keywords.- Approximation to frequency distributions, Process capability indices, Normality, data transformations, Simulation.

scholarly journals Process Capability Indices as Fuzzy Numbers

2016 ◽  
Vol 34 (4) ◽  
Author(s):  
Abbas Parchami ◽  
Mashaallah Mashinchi ◽  
Ali Reza Yavari ◽  
Hamid Reza Maleki
Keyword(s):  
Fuzzy Numbers ◽  
Process Capability ◽  
Process Capability Indices ◽  
Manufacturing Processes ◽  
Membership Functions ◽  
Specification Limits ◽  
Fuzzy Process ◽  
Capability Indices ◽  
Fuzzy Specification Limits ◽  
Fuzzy Process Capability

Most of the traditional methods for assessing the capability of manufacturing processes are dealing with crisp quality. In this paper we discuss the fuzzy quality and introduce fuzzy process capability indices, where instead of precise quality we have two membership functions for specification limits. These indices are necessary when the specification limits are fuzzy and they are helpful for comparing manufacturing processes with fuzzy specification limits. Some interesting relations among the introduced indices are obtained. Numerical examples are given to clarify the method.

Predict and Assure the Matchable Degree in Selective Assembly via PCI-based Tolerance

1999 ◽  
Vol 121 (3) ◽  
pp. 494-500 ◽  
Author(s):  
Yu Zhang ◽  
X. Daniel Fang
Keyword(s):  
Diesel Engine ◽  
Process Model ◽  
Process Capability Indices ◽  
Manufacturing Processes ◽  
Process Performance ◽  
Selective Assembly ◽  
Statistical Process ◽  
Quality Requirements ◽  
Capability Indices

Selective assembly is a method to achieve high precision fit using low precision parts. The current practices in selective assembly lack a physical process model to guide the design and process of mating parts, consequently, selective assembly is often a nightmare for manufacturing people and historically an event to be avoided. Process capability indices (PCIs) are the widely accepted process parameters, functioning as a measure of process performance, such as process centering, process spread, process bias, etc. In this paper, the concept of PCI-based tolerance is proposed as an interface between quality requirements and statistical process control (SPC) parameters. An analytical model involving PCI-based tolerance is developed to predict and assure the matchable degree in selective assembly. A detailed case study of the fit of dry liners and cylinder blocks in a diesel engine is presented. As matchable degree and other process quality requirements can be assured at the stage of design by introducing PCI-based tolerance, the process of selective assembly can be improved significantly as an effective way to achieve precision assembly with economical manufacturing processes.

Sign in / Sign up

Export Citation Format

Share Document