A process capability index for zero-inflated processes

The proportion of zero defect (ZD) outputs is as an integral characteristic of a zero-inflated (ZI) process or high quality process. Different ZI processes can almost equally satisfy the same USL of number of defects but they can produce substantially different proportions of ZD products. The application of conventional method for process capability evaluation fails to discriminate these processes because in the conventional method, the process capability is evaluated taking into consideration the USL of number of defects only. In this paper, a new measure of process capability for ZI processes is proposed that can truly discriminate different ZI processes taking into account the USL of number of defects as well as the proportion of ZD units produced in these processes. In the proposed approach, at first a measure of process capability index (PCI) with respect to the USL is computed, and then the overall PCI is obtained by multiplying it with an appropriately defined multiplying factor. A real-life application is presented

Evaluating capability of a bivariate zero-inflated poisson process

A zero-inflated Poisson (ZIP) distribution is commonly used for modelling zero-inflated process data with single type of defect, and for developing appropriate tools for instituting statistical process control of manufacturing processes. However, in reality, such manufacturing scenarios are very common where more than one type of defect can occur. For example, occurrences of defects like solder short circuits (shorts) and absence of solder (skips) are very common on printed circuit boards. In literature, different forms of bivariate zero-inflated Poisson (BZIP) distributions are proposed, which can be used for modelling the manufacturing scenarios where two types of defects can occur. Control charts are designed for monitoring for such processes using BZIP models. Although evaluation of capability is an integral part of statistical process control of a manufacturing process, researchers have given very little effort on this aspect of zero-inflated processes. Only a few articles attempted to evaluate the capability of a univariate zero-inflated process and no work is reported on evbaluating capability of a bivariate zero-inflated process. In this paper, a methodology for measuring capability of a bivariate zero-inflated process is presented. The proposed methodology is illustrated using two case studies.&nbsp

Optimization of Multiple Responses of Ultrasonic Machining (USM) Process: A Comparative Study

Ultrasonic machining (USM) process has multiple performance measures, e.g. material removal rate (MRR), tool wear rate (TWR), surface roughness (SR) etc., which are affected by several process parameters. The researchers commonly attempted to optimize USM process with respect to individual responses, separately. In the recent past, several systematic procedures for dealing with the multi-response optimization problems have been proposed in the literature. Although most of these methods use complex mathematics or statistics, there are some simple methods, which can be comprehended and implemented by the engineers to optimize the multiple responses of USM processes. However, the relative optimization performance of these approaches is unknown because the effectiveness of different methods has been demonstrated using different sets of process data. In this paper, the computational requirements for four simple methods are presented, and two sets of past experimental data on USM processes are analysed using these methods. The relative performances of these methods are then compared. The results show that weighted signal-to-noise (WSN) ratio method and utility theory (UT) method usually give better overall optimisation performance for the USM process than the other approaches

Feature-based decision rules for control charts pattern recognition: A comparison between CART and QUEST algorithm

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance

Assessing effectiveness of the various performance metrics for multi-response optimization using multiple regression

a b s t r a c t Several methods for optimization of multiple response problems using planned experimental data have been proposed in the literature. Among them, an integrated approach of multiple regression-based optimization using an overall performance criteria has become quite popular. In this article, we examine the effectiveness of five performance metrics that are used for optimization of multiple response problems. The usefulness of these performance metrics are compared with respect to a utility measure, namely, the expected total non-conformance (NC), for three experimental datasets taken from the literature. It is observed that multiple regression-based weighted signal-to-noise ratio as a performance metric is the most effective in finding an optimal solution for multiple response problems

Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

A dynamic system differs from a static system in that it contains signal factor and the target value depends on the level of the signal factor set by the system operator. The aim of optimizing a multi-response dynamic system is to find a setting combination of input controllable factors that would result in optimum values of all response variables at all signal levels. The most commonly used performance metric for optimizing a multi-response dynamic system is the composite desirability function (CDF). The advantage of using CDF is that it is a simple unit less measure and it has a good foundation in statistical practice. However, the problem with the CDF is that it does not consider the variability of the individual response variables. Moreover, if the specification limits for the response variables are not provided the CDF cannot be computed. In this paper, a new performance metric for multi-response dynamic system, called multiple regression-based weighted signal-to-noise ratio (MRWSN) is proposed, which overcome the limitations of CDF. Two sets of experimental data on multi-response dynamic systems, taken from literature, are analysed using both CDF-based and the proposed MRWSN-based approaches for optimization. The results show that the MRWSN-based approach also results in substantially better optimization performance than the CDF-based approach

A FRAMEWORK FOR PERFORMANCE EVALUATION AND MONITORING OF PUBLIC HEALTH PROGRAM USING COMPOSITE PERFORMANCE INDEX

A public health program (PHP) taken up by the government of a country refers to all organized measures to prevent disease and promote health among the population, by providing different planned cares/services to the people. Usually, the target population for different PHP are different. The basic requirement for success of a PHP is to ensure that all the planned cares/services are reached to each member of the target population. Therefore, the important performance measures for a PHP are the implementation status of all the planned cares/services under the PHP. However, management and monitoring of a PHP become quite difficult by interpreting separately the information contained in a large number of performance measures. Therefore, usually a metric, called composite performance index (CPI), is evaluated to understand the overall performance of a PHP. However, due a scaling operation involved in the CPI computation procedure, the CPI value does not reveal the true overall implementation status of a PHP and consequently, it is effective for management of a PHP. This paper presents a new approach for CPI computation, in which scaling/normalization of the performance variables is not required and therefore, it can be used for monitoring the true overall implementation status of a PHP in a region. A systematic approach for monitoring a PHP using the CPI values is proposed and applied for monitoring the maternal and child healthcare (MCH) program. The results are found effective towards continuous improvement of implementation status