Research on Statistical Process Control of Packaging & Printing Quality Management

2011 ◽  
Vol 467-469 ◽  
pp. 13-18
Author(s):  
Ying Zhe Xiao ◽  
Ya Nan Huang
Keyword(s):  
Quality Control ◽  
Quality Management ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Basic Tool ◽  
Statistical Process ◽  
Printing Quality ◽  
And Control ◽  
Development Course

This paper states not only the development course of quality management but also the actuality that the packaging & printing enterprise confronts. In addition, it explains the necessity of applying SPC. The first, it is discussed and studied the basic tool of SPC-control chart for statistical process. Based on this way, -R control chart is used to analyze and control the overprint precision. According to these control charts, the spot staffs can find the deficiencies in the quality control itself by finding the correlative process fluctuation and the slow variation in time. In addition, SPC provides objective bases for the quality management personnels to assess semi-products or products quality.

Statistical Process and Quality Control

2000 ◽  
pp. 233-244
Keyword(s):  
Quality Control ◽  
Quality Improvement ◽  
Quality Management ◽  
Total Quality Management ◽  
Statistical Process Control ◽  
Control Charts ◽  
Statistical Testing ◽  
Total Quality ◽  
Statistical Process ◽  
Sampling Plans

Abstract This chapter provides an introduction to statistical process control and the concept of total quality management. It begins with a review of quality improvement efforts in the extrusion industry and the considerations involved in developing sampling plans and interpreting control charts. It then lays out the steps that would be followed in order to implement statistical testing for billet casting, die performance, or any other process or variable that impacts extrusion quality. The chapter concludes with an overview of the fundamentals of total quality management.

A Conceptual Methodology for Recognition of Constrained Control Chart Patterns

2013 ◽  
Vol 845 ◽  
pp. 696-700
Author(s):  
Razieh Haghighati ◽  
Adnan Hassan
Keyword(s):  
Pattern Recognition ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Fault Tolerant ◽  
Constrained Control ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Control Chart Pattern ◽  
Chart Patterns

Traditional statistical process control (SPC) charting techniques were developed to monitor process status and helping identify assignable causes. Unnatural patterns in the process are recognized by means of control chart pattern recognition (CCPR) techniques. There are a broad set of studies in CCPR domain, however, given the growing doubts concerning the performance of control charts in presence of constrained data, this area has been overlooked in the literature. This paper, reports a preliminary work to develop a scheme for fault tolerant CCPR that is capable of (i) detecting of constrained data that is sampled in a misaligned uneven fashion and/or be partly lost or unavailable and (ii) accommodating the system in order to improve the reliability of recognition.

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Inspection in Process Control

1998 ◽  
Vol 42 (16) ◽  
pp. 1170-1174
Author(s):  
Somchart Thepvongs ◽  
Brian M. Kleiner
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Signal Detection ◽  
Control Chart ◽  
Control Charts ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Statistical Quality Control ◽  
Total Quality ◽  
Statistical Process

Consistent with the precepts of total quality control and total quality management, there has been a resource shift from incoming and outgoing inspection processes to statistical quality control of processes. Furthermore, process control operators are responsible for their own quality, necessitating the in-process inspection of components. This study treated the statistical process control task of “searching” control charts for out-of-control conditions as an inspection task and applied the Theory of Signal Detection to better understand this behavior and improve performance. Twelve subjects participated in a research study to examine how the portrayal of control chart information affected signal detection theory measures. The type of display did not have a significant effect on the sensitivity and response criterion of subjects. These results are discussed in terms of the applicability of Signal Detection Theory in control chart decision making as well as implications on display design.

TOTAL QUALITY MANAGEMENT USING PARETO DIAGRAMS AND STATISTICAL PROCESS CONTROL CHARTS (SPC) APPLIED TO PRECAST BEAMS

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Mifta Priyanto
Keyword(s):  
Quality Management ◽  
Process Control ◽  
Total Quality Management ◽  
Statistical Process Control ◽  
Control Charts ◽  
Range Limit ◽  
Total Quality ◽  
Statistical Process ◽  
Process Control Charts ◽  
Statistical Process Control Charts

This paper presents the application of Total Quality Management Method using Pareto diagrams and Statistical Process Control charts (SPC). These tools can be applied to both the manufacturing and construction sectors. A Pareto diagram can figure out some of the dominant problems of the projects, and SPC can determine whether the data variation is within control limits. SPC can measure the quality of performance in learning curve using the upper-range limit and lower-range limit of the control analysis. A case study was conducted on a precast beams installation at a rental multi-story residential project in Jakarta, Indonesia. Based on the measurement, some data are outside of the control limit due to the problems identified in the Pareto diagram. Further analysis by measuring the Process Capability Ratio (Cp) produces a value <1, indicating that project management needs to be careful about process variation.

CONTROLE ESTATÍSTICO DE PROCESSO (CEP) APLICADO NA COLHEITA MECANIZADA DO SORGO

2018 ◽  
Vol 17 (3) ◽  
pp. 490
Author(s):  
JOÃO PAULO BARRETO CUNHA ◽  
MILA SOUZA CASTRO ◽  
ANDERSON GOMIDE COSTA ◽  
MURILO MACHADO DE BARROS ◽  
TULIO ALMEIDA MACHADO ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Maximum Level ◽  
Statistical Process ◽  
Forage Sorghum ◽  
Randomized Design ◽  
Completely Randomized Design ◽  
And Control ◽  
The Ideal

RESUMO - A colheita, sendo uma das principais etapas no processo produtivo, precisa manter as perdas dentro de um controle aceitável para que seja possível atingir o máximo nível de qualidade e produtividade. No presente estudo, objetivou-se avaliar as perdas quantitativas durante a colheita mecanizada do sorgo forrageiro por meio do controle estatístico de processo (CEP). O experimento foi arranjado em delineamento inteiramente casualizado (DIC), em que foi realizada a análise de variância para a verificação do efeito significativo da declividade e da velocidade operacional nas perdas, e, quando significativos, foi submetida ao teste de comparação de médias de Tukey a 5% de significância. Cartas sequenciais e cartas de controle para valores individuais e de amplitude móveis foram utilizadas como ferramentas de controle estatístico de processo para verificar o efeito da velocidade operacional nas perdas. Com base nos resultados obtidos é possível indicar que a faixa de velocidade operacional de 4 a 5 km h-1 apresentou a menor variação dos dados, não apresentando nenhum ponto fora do limite de controle, o que lhe conferiu a condição de faixa ideal para colheita. Com base na análise estatística houve maiores perdas no transporte à medida que se aumenta a faixa de declividade do terreno.Palavras-chave: colheita mecanizada, forragicultura, carta de controle, velocidade operacional. STATISTICAL PROCESS CONTROL (SPC) APPLIED IN THE MECHANIZED HARVEST OF SORGHUM  ABSTRACT - Harvesting is one of the main steps in the production process and it is necessary to keep the losses under control in order to reach the maximum level of quality and productivity. The present study aimed to evaluate the quantitative losses during the mechanized harvesting of forage sorghum using the statistical process control (SPC). The experiment was arranged in a completely randomized design (DIC), and analysis of variance was performed to verify the significant effect of declivity and operational velocity on losses, and the significant was submitted to the Tukey test at 5% significance. Sequential charts and control charts for individual and mobile amplitude values, composed of upper and lower control and average limits, were used as statistical process control tools to verify the effect of operational speed on losses. Based on the results obtained it is possible to indicate that the operational velocity range from 4 to 5 km h-1 presented the lowest variation of data, presenting no point outside of the control limit, being the ideal range for harvest. The statistical analysis showed higher losses in transportation as the slope of the terrain increased.Keywords: Mechanized harvesting, forage farming, control charts, operational speeds.

Statistical Process Control Technology Applied in ERP System

2011 ◽  
Vol 421 ◽  
pp. 461-464 ◽  
Author(s):  
Ying Ji Li ◽  
Wei Xi Ji
Keyword(s):  
Process Control ◽  
Production Process ◽  
Statistical Process Control ◽  
Control Chart ◽  
Nuclear Power ◽  
Analytical Data ◽  
Quality Data ◽  
Control Technology ◽  
Statistical Process ◽  
And Control

For the high and strict quality requirement in the manufacturing process of nuclear power parts, this paper is based on the combination of Statistical Process Control technology and the ERP quality management and control the production quality based on the control chart. PowerBuilder 9.0 and SQL Server2000 were used to design and develop the system while PowerBuilder 9.0 as front-end development tool and SQL Server2000 as back-end DBMS respectively. Firstly, collect the quality data of the production process (some important processes). Then, analysis these data and form control chart. Real-time monitor production process by the control charting to ensure the process is stability. Organic combination of SPC and ERP to improve and control the quality, not only enrich the analytical data of SPC, but also make up the ERP data to analysis and control quality data.

Statistical Process Control on Time Delay Feedback Adjustment Process

2011 ◽  
Vol 211-212 ◽  
pp. 305-309
Author(s):  
Hai Yu Wang
Keyword(s):  
Time Delay ◽  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Adjustment Process ◽  
Statistical Process ◽  
Controlled Processes ◽  
Monitoring Process ◽  
Set Up ◽  
And Control

Control chart can be designed to quickly detect small shifts in the mean of a sequence of independent normal observations. But this chart cannot perform well for autocorrelated process. The main goal of this article is to suggest a control chart method using to monitoring process with different time delay feedback controlled processes. A quality control model based on delay feedback controlled processes is set up. And the calculating method of average run length of control charts based on process output and control action of multiple steps delay MMSE feedback controlled processes is provided to evaluate control charts performance. A simple example is used to illustrate the procedure of this approach.

The Application Research of Statistical Process Control in Quality Management

2014 ◽  
Vol 926-930 ◽  
pp. 4000-4003
Author(s):  
Qiu Yan Liu ◽  
Jun Li Shi ◽  
Shi Yuan Xing
Keyword(s):  
Quality Management ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Process Capability ◽  
Control Technology ◽  
Application Research ◽  
Statistical Process ◽  
Intensity Index ◽  
Productive Process

By discussing the application of statistical process control technology in the print and package industry, we have been in control of the productive process, for focusing on the heat-seal intensity index of sauce package products, and utilized instability of the control chart discovering the instable in the process , to realize the pre-alarm function. The management makes the process update constantly, and enhances the process capability.

scholarly journals Surveillance of Cyclospora cayetanensis Epidemics in USA from Long-Term National Outbreaks Reporting System-Based Monitoring: An Observational Study Using Statistical Process Control Methodologies

2019 ◽  
Vol 2 (1) ◽  
pp. 4-9
Author(s):  
Mostafa Essam Ahmed Mostafa Eissa ◽  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Reporting System ◽  
Statistical Process ◽  
Pareto Analysis ◽  
Cyclospora Cayetanensis ◽  
The Usa ◽  
One Year ◽  
And Control

Cyclosporiasis epidemics are caused primarily by food contaminated essentially with Cyclospora cayetanensis protozoa from Phylum Apicomplexa. National Outbreaks Reporting System (NORS) provides comprehensive monitoring and records for outbreaks in the USA. The pattern of the microbial epidemics could be investigated using statistical process control (SPC) techniques including Pareto analysis and control charts. The incidence of this outbreak is higher in some states more than others, especially Florida and transmitted mainly through herbal food constituents as a vehicle. Process-behavior charts show disease patterns and trends with the rate of occurrence per day 14.4%. Most of illness cases tend to occur in the summer environment except for March in one-year due spiking in the number of affected individuals during a solitary outbreak episode.

Sign in / Sign up

Export Citation Format

Share Document