scholarly journals Customized Requirements Driven Multivariate Quality Control for Steelmaking Process

2012 ◽  
Vol 6-7 ◽  
pp. 474-480
Author(s):  
Jing Ping Yang ◽  
Wan Lei Wang ◽  
Jia Xu ◽  
Shou Fang Mi
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Process Model ◽  
Control Process ◽  
Principal Component ◽  
Multivariate Control Charts ◽  
Multivariate Quality Control ◽  
Quality Control Process ◽  
Pca Method ◽  
Multivariate Control

In this paper, a new SPC based quality control process model for steelmaking industry is established, in which a Customer Requirements Weighted-Principal Component Analysis (CRW-PCA) method is proposed, the multivariate control charts based on this method can make special emphasis on the controlling of steelmaking quality characters response to customer’s special requirements. Practices show that compared with the traditional PCA-based multivariate control chart, the multivariate control charts based on CRW-PCA is more adaptive to the needs of today’s process quality control of steelmaking due to the adequate consideration of customers’ requirements.

scholarly journals Analisis Pengendalian Kualitas Menggunakan Bagan Kendali Bivariat dengan Copula pada Industri Manufactur di Indonesia

2020 ◽  
Vol 6 (1) ◽  
pp. 27
Author(s):  
Tika Endah Lestari ◽  
Sri Susilawati Islam
Keyword(s):  
Quality Control ◽  
Distribution Function ◽  
Control Charts ◽  
Control Process ◽  
Simple Random Sampling ◽  
Statistical Quality Control ◽  
Consumer Decision Making ◽  
Manufacturing Companies ◽  
Statistical Quality ◽  
Quality Control Process

Product quality control is an important factor for the industrial world because good quality control and carried out continuously will be able to detect abnormal production results quickly, so that anticipatory action can be taken immediately. Quality is a major factor in consumer decision making before buying goods / services. The problem that occurs at this time in manufacturing companies in Indonesia is how the statistical quality control process can be applied properly. The purpose of this statistical analysis is to find out the statistical quality control process that is applied to manufacturing companies in Indonesia using bivariate control charts with copula. Copula is a function that combines a multivariate distribution function with a uniform one-dimensional marginal distribution function, in this condition the Copula used is the Archimedean Copula group. The method used in this data collection is a simple random sampling with the sample used are three manufacturing companies in Indonesia which covers the areas of Jakarta, Bandung and Makassar. The implementation of Copula in this control chart results in Frank Copula being the best Copula, this supports that the use of Copula in the quality control process has a good role

scholarly journals A STATISTICAL ANALYSIS OF QUALITY CONTROL PROCESS ON PAPER PRODUCTION: A Case Study of Bel Papyrus Limited, Ogba, Lagos State

2021 ◽  
Vol 2 (3) ◽  
pp. 24-33
Author(s):  
Ali, Hassana Oseiwu ◽  
Orumbe, Seth Obafemi
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Control Charts ◽  
Control Process ◽  
Quality Standard ◽  
Primary Data ◽  
Paper Production ◽  
Quality Control Process ◽  
Lagos State

This research is an analysis of quality control process on paper production on the soft roll production process of Bel Papyrus Ltd located in Ogba, Lagos State, Nigeria. The research was done with the aim of determining the conformity of the industry’s product to quality standard, identifying and eliminating the possible causes of variation in their production process, with reference to Percentage Elongation. The researchers used primary data in form of periodic laboratory test result done on soft rolls.Data presentations were made using simple statistical tools like Mean, Ranges, Standard Deviations, and Tables reflecting the primary data obtained at equal interval of production. The researchers made use of variable control charts for the purpose of analysis. The

scholarly journals Comparing the Performance of Several Multivariate Control Charts Based on Residual of Multioutput Least Square SVR (MLS-SVR) Model in Monitoring Water Production Process

2021 ◽  
Vol 2123 (1) ◽  
pp. 012018
Author(s):  
M Ahsan ◽  
T R Aulia
Keyword(s):  
Water Quality ◽  
Quality Control ◽  
Control Charts ◽  
Continuous Process ◽  
Quality Characteristics ◽  
Least Square ◽  
Support Vector ◽  
Water Quality Control ◽  
Multivariate Control Charts ◽  
Multivariate Control

Abstract Water that is used as the basic human need, requires a processing process to get it. Water quality control in Tirtanadi Water Treatment Plant is still univariate, while theoretically the quality characteristics of water quality are correlated and there is also an autocorrelation due to the continuous process. In this study, quality control is performed on three main variables of water quality characteristics, namely acidity (pH), chlorine residual (ppm), and turbidity (NTU) using several multivariate control charts based on Multioutput Least Square Support Vector Regression (MLS-SVR) residuals. MLS-SVR modelling is used to overcome and get rid of autocorrelation. The input results of the MLS-SVR model are specified from the significant lag of the Partial Autocorrelation Function (PACF), which in this study, is the first lag. The results of the MLS-SVR input model and the optimal combination of hyper-parameters produce residual values that have no autocorrelation anymore. The residuals are used to develop the Hotelling’s T 2, Multivariate Exponentially Weighted Moving Average (MEWMA), and Multivariate Cumulative Sum (MCUSUM) control charts. In phase I, we found that the processes are statically controlled. Meanwhile, in phase II, the monitoring results show that there are several out-of-control observations.

Quality Control-An Approach Applying Multivariate Control Charts during the Operation of Systems Involving Human Processes

2013 ◽  
Vol 17 (2) ◽  
pp. 204-212
Author(s):  
Matthew J. Mihalcin ◽  
Thomas A. Mazzuchi ◽  
Shahram Sarkani ◽  
Jason R. Dever
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Multivariate Control Charts ◽  
Multivariate Control

Quality Control of Orange Juice Using Electronic Nose

2015 ◽  
Vol 1131 ◽  
pp. 242-245
Author(s):  
Rungroj Maolanon ◽  
Winadda Wongwiriyapan ◽  
Sirapat Pratontep
Keyword(s):  
Quality Control ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Orange Juice ◽  
Fermentation Process ◽  
Control Process ◽  
Principal Component ◽  
Alcohol Concentration ◽  
Ethanol Level ◽  
Quality Control Process

Applications of electronic noses to classify the freshness of food and beverages by mimicking the olfactory perception are becoming widely recognized in food industries. For pasteurized orange juice, packaging and shelf-life are key factors for the quality control, which are generally inspected by the sensory stability and quality (odor, color, texture and taste) of the orange juice. An electronic nose based on five different commercial metal oxide gas sensors, a temperature sensor and a humidity sensor has been designed and constructed to examine the quality of orange juice as subjected to the fermentation process. The duration for a single measurement from an orange juice sample was approximately two minutes. The data acquisition of the voltage responses of the gas sensors were achieved via a microcontroller unit. The data classification was statistically analyzed by the “Principal Component Analysis (PCA)”. The Euclidean distance between two PCA groups was used as an indicator of ethanol concentration. The orange juice was laced with various concentrations of ethanol from 0.1 to 1.0% ethanol to simulate fermented orange juice at different stages. The objective was to characterize the freshness of orange juice by means of the ethanol level from the fermentation process. The results show a distinctive classification of the orange juice for an alcohol concentration lower than 0.1%. Thus the electronic nose offers a rapid, highly sensitive alternative for the quality control process.

scholarly journals Assessing Response Quality by Using Multivariate Control Charts for Numerical and Categorical Response Quality Indicators

2020 ◽  
Author(s):  
Jiayun Jin ◽  
Geert Loosveldt
Keyword(s):  
Quality Indicators ◽  
Control Charts ◽  
Principal Component ◽  
Nonparametric Bootstrap ◽  
Control Data ◽  
Response Quality ◽  
Multivariate Control Charts ◽  
Categorical Response ◽  
Multivariate Control ◽  
Component Scores

Abstract When assessing interview response quality to identify potentially low-quality interviews, both numerical and categorical response quality indicators (mixed indicators) are usually available. However, research on how to use them simultaneously is very rare. In the current article, we extend the application of conventional multivariate control charts to include response quality indicators that are of a mixed type. We analyze data from the eighth round of the European Social Survey in Belgium, characterized by six numerical and two categorical response quality indicators. First, we employ a principal component analysis mix procedure (PCA Mix) to transform the mixed quality indicators into principal components. The principal component scores are subsequently used to construct a Hotelling T2 statistic. To deal with the non-multivariate normal nature of the principal component scores obtained from the PCA Mix, a nonparametric bootstrap method is then applied to calculate the control limit for the T2 statistic. Second, we suggest tools to interpret an identified outlier in terms of finding the responsible original indicator(s). Third, we present a cyclic procedure for determining the “in-control” data, by iteratively removing the outliers until the process is considered as being in control. Lastly, we identify the most important indicators that discriminate the outliers from the in-control data. Our results imply that multivariate control charts based on relevant projection tools such as PCA Mix in combination with the bootstrap technique have great potential for use in evaluating interview response quality and identifying outliers.

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