scholarly journals The Concept of a Method Improving the Process of Aeronautical Geospatial Data Creation

2018 ◽  
Vol 46 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Ewa Dudek ◽  
Michał Kozłowski
Keyword(s):  
Control Charts ◽  
Geospatial Data ◽  
Quality And Safety ◽  
Legal Requirements ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
The Subject ◽  
Proposed Modification ◽  
Control Limits

Abstract This article is a continuation of the Authors’ study on the ways to ensure the quality and safety of aeronautical data and information. This time, however, the legal requirements for aeronautical data and information were briefly described and then the concept of purpose and scope of Shewhart control charts’ implementation, presented in [2], was broadened and it was proposed to refer the calculated upper (GLK) and lower (DLK) control limits to the requirements set out in the legal specification including in particular the accuracy values out of the Harmonised List from the Eurocontrol Specification [13]. In order to illustrate the proposed modification, an example of such a card for the measured aeronautical obstacles was presented, considering two cases: that these obstacles are located in Area 3 and in Area 2. Analysed issues will be the subject of Authors’ further study.

scholarly journals Outliers Detection Models in Shewhart Control Charts; an Application in Photolithography: A Semiconductor Manufacturing Industry

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 857 ◽  
Author(s):  
Ishaq Adeyanju Raji ◽  
Muhammad Hisyam Lee ◽  
Muhammad Riaz ◽  
Mu’azu Ramat Abujiya ◽  
Nasir Abbas
Keyword(s):  
Phase I ◽  
Outlier Detection ◽  
Control Charts ◽  
Semiconductor Manufacturing ◽  
Manufacturing Industry ◽  
Estimation Errors ◽  
Run Length ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Control Limits

Shewhart control charts with estimated control limits are widely used in practice. However, the estimated control limits are often affected by phase-I estimation errors. These estimation errors arise due to variation in the practitioner’s choice of sample size as well as the presence of outlying errors in phase-I. The unnecessary variation, due to outlying errors, disturbs the control limits implying a less efficient control chart in phase-II. In this study, we propose models based on Tukey and median absolute deviation outlier detectors for detecting the errors in phase-I. These two outlier detection models are as efficient and robust as they are distribution free. Using the Monte-Carlo simulation method, we study the estimation effect via the proposed outlier detection models on the Shewhart chart in the normal as well as non-normal environments. The performance evaluation is done through studying the run length properties namely average run length and standard deviation run length. The findings of the study show that the proposed design structures are more stable in the presence of outlier detectors and require less phase-I observation to stabilize the run-length properties. Finally, we implement the findings of the current study in the semiconductor manufacturing industry, where a real dataset is extracted from a photolithography process.

STATISTICAL PROCESS CONTROL FOR LOW NONCONFORMITY PROCESSES

1995 ◽  
Vol 02 (01) ◽  
pp. 15-22 ◽  
Author(s):  
T.N. GOH ◽  
M. XIE
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Exact Probability ◽  
Statistical Process ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Cumulative Count ◽  
Pattern Recognition Approach ◽  
Control Limits

Statistical process control of high quality products is an important issue in modern quality control applications because of the success of continuous improvement efforts worldwide. The conventional Shewhart control charts based on 3-sigma control limits tend to encounter certain practical and theoretical problems as “zero-defect” is approached. In this paper, we describe some general approaches to solving this problem, focusing on the control charts for nonconformities or defects. We suggest that for a moderate nonconformity process, the exact probability limits should be used. For a lower non-conformity process, a “pattern recognition” approach can be applied. Finally, for a near-zero nonconformity process, a modified approach based on the cumulative count of nonconformities can be used.

Shewhart Control Charts for Monitoring Reliability with Weibull Lifetimes

2014 ◽  
Vol 31 (8) ◽  
pp. 1565-1574 ◽  
Author(s):  
Alireza Faraz ◽  
Erwin M. Saniga ◽  
Cedric Heuchenne
Keyword(s):  
Control Charts ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Monitoring Reliability

scholarly journals One-sided Shewhart control charts for monitoring the ratio of two normal variables in short production runs

2021 ◽  
Vol 69 ◽  
pp. 273-289
Author(s):  
Kim Duc Tran ◽  
Qurat-Ul-Ain Khaliq ◽  
Adel Ahmadi Nadi ◽  
Thi Hien Nguyen ◽  
Kim Phuc Tran
Keyword(s):  
Control Charts ◽  
Shewhart Control Charts ◽  
Shewhart Control

scholarly journals Shewhart's control charts and process capability ratio applied to a sewage treatment station

2014 ◽  
Vol 34 (4) ◽  
pp. 770-779 ◽  
Author(s):  
Fábio Orssatto ◽  
Marcio A. Vilas Boas ◽  
Ricardo Nagamine ◽  
Miguel A. Uribe-Opazo
Keyword(s):  
Control Charts ◽  
Suspended Solids ◽  
Sewage Treatment ◽  
Oxygen Demand ◽  
Process Capability ◽  
Total Suspended Solids ◽  
Statistical Control ◽  
Environmental Legislation ◽  
Shewhart Control Charts ◽  
Shewhart Control

The current study used statistical methods of quality control to evaluate the performance of a sewage treatment station. The concerned station is located in Cascavel city, Paraná State. The evaluated parameters were hydrogenionic potential, settleable solids, total suspended solids, chemical oxygen demand and biochemical oxygen demand in five days. Statistical analysis was performed through Shewhart control charts and process capability ratio. According to Shewhart charts, only the BOD(5.20) variable was under statistical control. Through capability ratios, we observed that except for pH the sewage treatment station is not capable to produce effluents under characteristics that fulfill specifications or standard launching required by environmental legislation.

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