Attribute Quality Characteristics Research Articles

Kernel principal component analysis (PCA) control chart for monitoring mixed non-linear variable and attribute quality characteristics

The products are commonly measured by two types of quality characteristics. The variable characteristics measure the numerical scale. Meanwhile, the attribute characteristics measure the categorical data. Furthermore, in monitoring processes, the multivariate variable quality characteristics may have a nonlinear relationship. In this paper, the Kernel PCA control chart is applied to monitor the mixed (attribute and variable) characteristics with the nonlinear relationship. First, the Average Run Length (ARL) is utilized to evaluate the performance of the proposed chart. The simulation studies show that the proposed chart can detect the shift in process. For this case, the Radial Basis Function (RBF) kernel demonstrates the consistent performance for several cases studied. Second, the performance comparison between the proposed chart and the conventional PCA Mix chart is performed. Based on the results, it is known that the proposed chart performs better in detecting the small shift in process. Finally, the proposed chart is applied to monitor the well-known NSL KDD dataset. The proposed chart shows good accuracy in detecting intrusion in the network. However, it still produces more False Negatives (FN).

Taguchi's robust parameter design to analyse ordered categorical data using inverse omega transformation

The manufacturing industry is striving hard to gain an edge over its competitors as far as the product quality is concerned. The product may have both variable and attribute quality characteristics. The variable cases are addressed using Shewhart's variable control charts, process capability analysis etc. Most of the attribute cases are addressed using 'fraction defective'; this serves as a quality measure. When such attributes of the product are categorised according to their severity, then the same case may be dealt with as 'ordered categorical data'. This research work presents the use of Taguchi's parameter design to improve the product quality during spline hob operation of a shaft. The visual defects that occur during this operation are treated as ordered categorical data and the analysis of this data is carried out using inverse omega transformation. The paper has resulted in determining the optimum settings for the process parameters in the spline hob operation.

Multiple Deferred State Sampling Plan for Exponentiated New Weibull Pareto Distributed Mean Life Assurance

Abstract This article proposes a methodology of designing the multiple deferred state acceptance sampling plan for assuring lifetime of the products when the underlying distribution of lifetime is exponentiated new Weibull-Pareto. Inspection of the sample items is done based on attribute quality characteristics under a time truncated life test. A number of tables are constructed using attribute multiple deferred state acceptance sampling plans of some specified value for mean lifetime ratio that may be obtained under the exponentiated new Weibull-Pareto model. The consequence of misspecification of the shape parameter and the role of the number of successive lots needed for sentencing current lot are investigated. A procedure for implementing the proposed plan for life-testing applications is included. The performance of the proposed plan is compared in terms of the average sample number required as well as discriminating power.

Economic Design of Quick Switching Sampling System for Resubmitted Lots under Gamma-Poisson Distribution

In this paper, a method of designing quick switching sampling system for resubmitted lots for inspection of attribute quality characteristics based on the gamma-Poisson distribution is proposed. We determine the required parameters by using two-points on the operating characteristic curve approach such that both producer’s and the consumer’s risks are satisfied for some specified values of acceptable quality level and limiting quality level. Tables are constructed for optimal sample size and two acceptance numbers of the proposed sampling system. The relative efficiency of the proposed system over conventional plans is also discussed. In addition, an economic design of the above mentioned sampling system to minimize total cost is also investigated. Consequently, it is proved that the proposed system minimizes the total inspection cost and average total inspection.

Multivariate Control Chart Based on Kernel PCA for Monitoring Mixed Variable and Attribute Quality Characteristics

The need for a control chart that can visualize and recognize the symmetric or asymmetric pattern of the monitoring process with more than one type of quality characteristic is a necessity in the era of Industry 4.0. In the past, the control charts were only developed to monitor one kind of quality characteristic. Several control charts were created to deal with this problem. However, there are some problems and drawbacks to the conventional mixed charts. In this study, another approach is used to monitor mixed quality characteristics by applying the Kernel Principal Component Analyisis (KPCA) method. Using the Hotelling’s T2 statistic, the kernel PCA mix chart is proposed to simultaneously monitor the variable and attribute quality characteristics. Due to its ability to estimate the asymmetric pattern of the mixed process, the kernel density estimation (KDE) used in the proposed chart has successfully estimated the control limits that produce ARL0 at about 370 for α=0.00273. Through several experiments based on the proportion of the attribute characteristics and kernel functions, the proposed chart demonstrates better performance in detecting outlier and shift in the process. When it is applied to monitor the synthetic data, the proposed chart can detect the shift accurately. Additionally, the proposed chart outperforms the performance of the conventional mixed chart based on PCA mix by producing lower false alarm with more accurate detection of out of control processes.

Economic design of quick switching sampling system for resubmitted lots

This paper proposes a quick switching sampling system for the inspection of attributes quality characteristics for resubmitted lots. The optimal parameters for both fraction non conforming items and fraction non conformities of the proposed sampling system are determined using an optimization procedure so that producer’s risk and consumer’s risk are simultaneously satisfied. Tables are also constructed for the selection of parameters for specified AQL and LQL. The advantage of the proposed plan over the existing plan is discussed and illustrate. An economic design of the proposed sampling system is also discussed and shown that the proposed sampling system has minimum total cost and average total inspection compared to other existing sampling plans.

Implementation of Statistical Process Control (SPC) in the Sewing Section of Garment Industry for Quality Improvement

Abstract The main aim of this research was to implement appropriate Statistical Process Control (SPC) techniques for quality characteristics on sewing floor of garment Industry. Among different SPC quality improvements tools, control charts have been selected. After analyzing and selecting different critical parameters based on company and customer requirements, the X-bar and R charts for variable and c-charts for attribute quality characteristics have been identified and implemented in the trouser sewing lines for quality improvement. The check points for selected control chart implementation have also been designed. Remedial action plans for the occurred special cause variations and process stability were developed. The project incorporated theoretical and on-job training schemes for different quality team members, to understand the SPC concept and its implementation procedure. After implementation, significant improvements in the sewing section were achieved. The four months analysis before and after implementation of the SPC tools showed that the rejection percentage was reduced from 9.141% to 6.4%. Successful implementation of the result of this project can significantly improve process performance of other similar manufacturing units with appropriate modification.

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

ABSTRACTTwo types of control charts exist based on different quality characteristics: variable and attribute. These characteristics are commonly monitored using separate procedures. Only a few studies focused on the utilization of control charts to monitor a process with mixed characteristics. This study develops a new concept of the control chart based on a Principal Component Analysis (PCA) Mix, that is a PCA method that can jointly handle continuous and categorical data. The Kernel Density Estimation (KDE) method is used to estimate the control limit. Through simulation studies, the performance of the proposed chart is evaluated using the Average Run Length (ARL). control limits obtained from KDE produce a stable ARL0 at ~ 370 for For the shifted process, the proposed chart demonstrates excellent performance for an appropriate number of principal components used. Applications of the simulated process and real cases show that the proposed chart is sensitive to monitoring the shifted process.

Optimal designing of a new mixed variable lot-size chain sampling plan based on the process capability index

ABSTRACTIn this paper, a new mixed sampling plan based on the process capability index (PCI) Cpk is proposed and the resultant plan is called mixed variable lot-size chain sampling plan (ChSP). The proposed mixed plan comprises of both attribute and variables inspections. The variable lot-size sampling plan can be used for inspection of attribute quality characteristics and for the inspection of measurable quality characteristics, the variables ChSP based on PCI will be used. We have considered both symmetric and asymmetric fraction non conforming cases for the variables ChSP. Tables are developed for determining the optimal parameters of the proposed mixed plan based on two points on the operating characteristic (OC) approach. In order to construct the tables, the problem is formulated as a non linear programming where the average sample number function is considered as an objective function to be minimized and the lot acceptance probabilities at acceptable quality level and limiting quality level under the OC curve are considered as constraints. The practical implementation of the proposed mixed sampling plan is explained with an illustrative real time example. Advantages of the proposed sampling plan are also discussed in terms of comparison with other existing sampling plans.

Designing of a mixed-chain sampling plan based on the process capability index with chain sampling as the attributes plan

ABSTRACTIn this article, we propose a new mixed chain sampling plan based on the process capability index Cpk, where the quality characteristic of interest having double specification limits and follows the normal distribution with unknown mean and variance. In the proposed mixed plan, the chain sampling inspection plan is used for the inspection of attribute quality characteristics. The advantages of this proposed mixed sampling plan are also discussed. Tables are constructed to determine the optimal parameters for practical applications by formulating the problem as a non linear programming in which the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at acceptable quality level and limiting quality level under the operating characteristic curve. The practical application of the proposed mixed sampling plan is explained with an illustrative example. Comparison of the proposed sampling plan is also made with other existing sampling plans.

Design and development of relational chain sampling plans [RChSP(0, i)] for attribute quality characteristics

The purpose of this research article is to provide a novel algorithm for the Relational Chain Sampling Inspection in order to emphasize quality products from the production sources. It is evident t...

A Modified Single Sampling Plan for the Inspection of Attribute Quality Characteristics

In this manuscript, a modified single sampling plan is proposed for the inspection of products in which the nonconforming items can be classified in to two categories namely critical and non-critical; and explained with the help of industrial example. The operating procedure of this plan is also proposed and the performance measures such as the probability of acceptance, average sample number, average total inspection and average out going quality are also derived. The optimal parameters are determined which will have minimum sample size. The efficiency of the proposed plan is also discussed over the conventional single sampling plan. The extensive tables for selecting a modified single sampling plan based on AQL and LQL are provided for both Binomial and Poisson distributions and explained with the help of industrial data.

Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation

In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, first we use an artificial neural network (ANN)-based method in the literature for detecting the variance shifts as well as diagnosing the sources of variation in the multivariate-attribute processes. Then, based on the quality characteristics responsible for the out-of-control state, we propose a modular model based on the ANN for estimating the time of step change in the multivariate-attribute process variability. We also compare the performance of the ANN-based estimator with the estimator based on maximum likelihood method (MLE). A numerical example based on simulation study is used to evaluate the performance of the estimators in terms of the accuracy and precision criteria. The results of the simulation study show that the proposed ANN-based estimator outperforms the MLE estimator under different out-of-control scenarios where different shift magnitudes in the covariance matrix of multivariate-attribute quality characteristics are manifested.

Online Monitoring and Fault Diagnosis of Multivariate-attribute Process Mean Using Neural Networks and Discriminant Analysis Technique

In some statistical process control applications, the process data are not Normally distributed and characterized by the combination of both variable and attributes quality characteristics. Despite different methods which are proposed separately for monitoring multivariate and multi-attribute processes, only few methods are available in the literature for monitoring multivariate-attribute processes. In this paper, we develop discriminant analysis technique for monitoring the mean vector of correlated multivariate-attribute quality characteristics in the first module. Then in the second module, a novelty approach based on the combination of artificial neural network (ANN) and discriminant analysis is proposed for detecting different mean shifts. The proposed approach is also able to diagnose quality characteristic(s) responsible for out-of-control signals after detecting different step mean shifts. A numerical example based on simulation is given to evaluate the performance of the proposed methods for detection and diagnosis purposes. The detecting performance of the second module is also compared with the extended T2 control chart and with the extension of an ANN in the literature. The results confirm that the proposed method outperforms both methods.

Multivariate multinomial T2 control chart using fuzzy approach

Quality of a product is often measured through various quality characteristics generally correlated. Multivariate control charts are a response to the need for quality control in such situations. If quality characteristics are qualitative, it sometimes happens that the product quality is defined by linguistic variables – where quality levels are represented by some specific words – and product units are classified into several linguistic forms categories, depending on the degree of fulfilment of expectations, creating a situation of fuzzy classifications. This study first reviews the concepts found in the literature on the development of fuzzy multivariate control charts. We propose a method to control these fuzzy quality evaluations, with correlated multiple attributes quality characteristics, through the use of a Hotelling T2 control chart.

New Attributes and Variables Control Charts under Repetitive Sampling

New control charts under repetitive sampling are proposed, which can be used for variables and attributes quality characteristics. The proposed control charts have inner and outer control limits so that repetitive sampling may be needed if the plotted statistic falls between the two limits. Particularly, the new np and variable X-bar control charts under repetitive sampling are considered in detail. The in-control and out-of-control average run lengths are analyzed according to various process shifts. The performance of the proposed control charts is compared with the existing np and the X-bar control charts in terms of the average run lengths.

Monitoring correlated variable and attribute quality characteristics based on NORTA inverse technique

In some statistical process control applications, quality of a process or a product is characterised and monitored based on a variable or an attribute quality characteristic. However, sometimes a vector of variables or attributes describes the quality of a process. Likewise, in some cases, quality of a process or a product is characterised by a combination of several correlated variables and attributes. To the best of our knowledge, there is no method in monitoring multivariate-attribute processes in spite of numerous studies in multivariate and multi-attribute control charts. This paper describes a method to monitor a process with multiple correlated variable and attribute quality characteristics. In the proposed method, we utilise NORTA inverse technique to design a scheme in monitoring multivariate-attribute processes. First, NORTA inverse method transforms the data to a multivariate normal distribution, and then we apply multivariate control charts such as T2 and MEWMA for transformed data. The performance of the proposed method considering both T2 and MEWMA charts is investigated by using simulation studies in terms of average run length criterion.

An extension of fuzzy P-control chart based on $\alpha-$level fuzzy midrange

Control charts are one of the most important tools in statistical process control that lead to improve quality processes and ensure required quality levels. In traditional control charts, all data should be exactly known, whereas there are many quality characteristics that cannot be expressed in numerical scale, such as characteristics for appearance, softness, and color. Fuzzy sets theory is powerful mathematical approach to analyze uncertainty, ambiguous and incomplete that can linguistically define data in these situations. Fuzzy control charts have been extended by converting the fuzzy sets associated with linguistic or uncertain values into scalars regarded as representative values. In this paper, we develop a new fuzzy control chart for monitoring attribute quality characteristics based on $\alpha-$level fuzzy midrange approach. Finally, the performance and comparative results of the proposed fuzzy control chart is measured in terms of average run length (ARL) by Mont Carlo simulation.

Monitoring multivariate–attribute processes based on transformation techniques

Control charts are widely used in monitoring the quality of a product or a process. In most of the cases, quality of a product or a process can be characterized by two or more correlated quality characteristics. Many control charts have been proposed for monitoring multivariate or multi-attribute quality characteristics, separately, but sometimes the correlated variables and attribute quality characteristics represents the quality of a process. In this paper, the use of four transformation methods is proposed to monitor the multivariate–attribute processes. In the first one, the distribution of correlated variables and attribute quality characteristics are transformed to approximate multivariate normal distribution, and then the transformed data are monitored by multivariate control charts including T 2 and MEWMA. Based on the second transformation method, the correlated variables and attribute quality characteristics are transformed, such that the correlation between the quality characteristics approaches to zero, then univariate control charts are used in monitoring the transformed data. In the third and fourth proposed methods, a combination of two transformation methods is used to make the quality characteristics independent and to transform them to normal distribution. The difference between the third and fourth method is the order of using the transformation techniques. The performance of the proposed methods is evaluated by using simulation studies in terms of average run length criterion. Finally, the proposed approach is applied to a real dataset.

An Efficient Novel Compensatory Multi-attribute Control Chart for Correlated Multinomial Processes

Monitoring multi-attribute processes is an important issue in many quality control environments. Almost all the priory proposed control charts utilize equal weights for each Attribute Quality Characteristics (AQCs). In such condition, there is no priority among AQCs. But in real-world, compensatory may exist. Hence due to some applied reasons such as function or efficiency, unequal weights for each AQC are possible. This study proposed a novel efficient control chart for simultaneous monitoring of weighted AQC when data expressed by linguistic terms. Correspondingly a new procedure to interpret out-of-control signals is presented. Performance and comparison advantage of the proposed control chart is measured in terms of Average Run Length (ARL) using a real case which priory was expressed. Consequences displayed that considering weight could efficiently extend the prior research for practical circumstancese.