Measuring process capability for bivariate non-normal process using the bivariate burr distribution

As is well known, process capability analysis for more than one quality variables is a complicated and sometimes contentious area with several quality measures vying for recognition. When these variables exhibit non-normal characteristics, the situation becomes even more complex. The aim of this paper is to measure Process Capability Indices (PCIs) for bivariate non-normal process using the bivariate Burr distribution. The univariate Burr distribution has been shown to improve the accuracy of estimates of PCIs for univariate non-normal distributions (see for example, [7] and [16]). Here, we will estimate the PCIs of bivariate non-normal distributions using the bivariate Burr distribution. The process of obtaining these PCIs will be accomplished in a series of steps involving estimating the unknown parameters of the process using maximum likelihood estimation coupled with simulated annealing. Finally, the Proportion of Non-Conformance (PNC) obtained using this method will be compared with those obtained from variables distributed under the bivariate Beta, Weibull, Gamma and Weibull-Gamma distributions

Process performance evaluation using evolutionary algorithm

Nowadays every business is using different quantitative measures and techniques to assess performance of their products / services. It is well known that different manufacturing processes very often manufacture products with quality characteristics that do not follow normal distribution. In such cases, fitting a known non-normal distribution to these quality characteristics would lead to erroneous results. Furthermore, there is always more than one characteristic Critical to Quality (CTQ) in the process outcomes and very often these quality characteristics are correlated with each other. In this paper, we assess performance of such a bivariate process data which is non-normal as well as correlated. We will use the geometric distance approach to reduce the dimension of the correlated non-normal bivariate data and then fit Burr distribution to the geometric distance variable. The optimal parameters of the fitted Burr distribution are estimated using Evolutionary Algorithm (EA). The results are compared with those using Simulated Annealing (SA) algorithm. The proportion of nonconformance (PNC) for process measurements is then obtained by using the fitted Burr distributions based on the two methods. The results based on both search algorithms are then compared with the exact proportion of nonconformance of the data. Finally, a case study using real data is presented

Titanium Dioxide Nanoparticles: Synthesis, X-Ray Line Analysis and Chemical Composition Study

TiO2 nanoparticleshave been synthesized by the sol-gel method using titanium alkoxide and isopropanolas a precursor. The structural properties and chemical composition of the TiO2 nanoparticles were studied usingX-ray diffraction, scanning electron microscopy, and X-ray photoelectron spectroscopy.The X-ray powder diffraction pattern confirms that the particles are mainly composed of the anatase phase with the preferential orientation along [101] direction.The physical parameters such as strain, stress and energy density were investigated from the Williamson- Hall (W-H) plot assuming a uniform deformation model (UDM), and uniform deformation energy density model (UDEDM). The W-H analysis shows an anisotropic nature of the strain in nanopowders. The scanning electron microscopy image shows clear TiO2 nanoparticles with particle sizes varying from 60 to 80nm. The results of mean particle size of TiO2 nanoparticles show an inter correlation with the W-H analysis and SEM results. Our X-ray photoelectron spectroscopy spectra show that nearly a complete amount of titanium has reacted to TiO2

Assessment of the physical characteristics and stormwater effluent quality of permeable pavement systems containing recycled materials

This paper evaluates the physical characteristics of two recycled materials and the pollutant removal efficiencies of four 0.2 m2 tanked permeable pavement rigs in the laboratory, that contained either natural aggregates or these recycled materials in the sub-base. The selected recycled materials were Crushed Concrete Aggregates (CCA) and Cement-bounded Expanded Polystyrene beads (C-EPS) whilst the natural aggregates were basalt and quartzite. Natural stormwater runoff was used as influent. Effluent was collected for analysis after 7–10 mins of discharge. Influent and effluent were analysed for pH, Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electroconductivity (EC), turbidity, Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Nitrate-Nitrogen (NO3-N), reactive phosphorous (PO43-) and sulphates (SO42-). Both CCA and C-EPS had suitable physical properties for use as sub-base materials in PPS. However, C-EPS is recommended for use in pavements with light to no traffic because of its relatively low compressive strength. In terms of pollutant removal efficiencies, significant differences (p 0.05) were found with respect to TSS, turbidity, COD and NO3-N. Effluent from rigs containing CCA and C-EPS saw significant increases in pH, EC and TDS measurements whilst improvements in DO, TSS, turbidity, COD, PO43- and SO42- were observed. All mean values except pH were, however, within the Maximum Permissible Levels (MPLs) of water pollutants discharged into the environment according to the Trinidad and Tobago Environmental Management Authority (EMA) or the United States Environmental Protection Agency (US EPA). In this regard, the CCA and C-EPS performed satisfactorily as sub-base materials in the permeable pavement rigs. It is noted, however, that further analysis is recommended through leaching tests on the recycled materials

Multivariate control charts for surgical procedures

Patient's progress in the Intensive Care Unit is characterised by more than one quality characteristics. This paper employs univariate and multivariate control charts to monitor patient progress in the Intensive Care Unit. A definitive comparison is made, between the performance of univariate and multivariate control chart methods, in the monitoring of the patient recovery process

Modelling weight of a newborn based on baby's characteristics for low birth weight babies

Neonatal mortality rate (NMR) is an increasingly important public health issues in many developing countries. Neonatal death now accounts for about two-thirds of the eight million infant deaths that occur globally each year. It is well-documented that low birth weight (LBW) is the most significant factor influencing NMR. This paper deploys multi-regression models to identify the significant factors for forecasting the weight of the LBW babies. The model explores the relationship between weights, baby's characteristics, gestation age and mother pre-pregnancy BMI. The results indicate that 65.9% of the variations in the weight of the LBW babies can be explained by baby's characteristics such as the height, head and chest circumferences, the gestation age and mother's BMI. The proposed model was then used to estimate the recorded weights together with their corresponding 95% confidence and predication interval. Analysis of the prediction errors shows that the mean prediction error for the recorded data is one gram. The research is based on a case study in Indonesia intended to improve the mortality rate

On the estimating burr XII distribution Parameters

Burr XII distribution plays an important role in reliability modeling, risk analyzing and process capability estimation. However, estimating two parameters of the Burr XII distribution, i.e., c and k, is a complicated task and using conventional methods is not straightforward. In this paper a neural network to estimate Burr XII parameters is presented. The inputs of proposed neural network are skewness and kurtosis. The performance of proposed methods is evaluated in different simulation examples

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized 16 by a relation (profile) between a response variable and one or more explanatory variables. Such profiles 17 can be modeled using linear or nonlinear regression models. In this paper we use artificial neural net- 18 works to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial 19 neural networks are developed to monitor linear profiles. Their efficacies are assessed using average 20 run length criterion