The contribution of statistical processes in the control of technological processes

2021 ◽  
Vol 66 (1) ◽  
pp. 5-16
Author(s):  
Olga-Ioana Amariei ◽  
Codruța-Oana Hamat ◽  
Alexandru-Victor Amariei
Keyword(s):  
Quality Control ◽  
Control Chart ◽  
Manufacturing Process ◽  
Quality Characteristic ◽  
Screw Head ◽  
Technological Processes ◽  
Quality Control Chart ◽  
The Way

In this paper, a manufacturing process is analyzed, having as quality characteristic the “height of the screw head”, using analyzes and representative diagrams. Based on this case study, the way to solve these types of problems using the Quality Control Chart module of the WinQSB program, as well as the XLSTAT program is presented.

scholarly journals A Fuzzy Control Chart Approach for Attributes and Variables

2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
New Approach ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Chart

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

scholarly journals Study on the Quality Control Chart for Determination of Soil Organic Carbon by Instrumental Analysis

2020 ◽  
Vol 206 ◽  
pp. 01004
Author(s):  
Zhang Fuling ◽  
Zhang Zhuo ◽  
Diao Er-long ◽  
Zhao Meiliang ◽  
Liu Menglin ◽  
...  
Keyword(s):  
Quality Control ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Control Chart ◽  
Control Line ◽  
Organic Carbon Content ◽  
Content Determination ◽  
Warning Limit ◽  
Quality Control Chart

【Objective】To ensure that each analysis step is in the monitoring state. The quality control chart is used to control the process of soil organic carbon content determination, and the reasons for drifting or exceeding the allowable value of the result data can be found out in time.【Method】The content of soil organic carbon in quality control samples was determined by instrumental analysis, and the quality control chart was drawn based on the determination data in Excel 2007, which was used for the quality control of the soil organic carbon content determination process.【Result】The control line of the mean control chart was 41.94% ~ 40.51%, and the warning limit was 41. 70% ~ 41. 74%. The control line range of the range control chart is 0.00% ~ 2.75%, and the warning limit is 0.00% ~ 2. 12%.【Conclusion】The quality control chart method is simple to operate,easy to master, and can timely find the abnormal points or abnormal trends of data, which has high application value in test analysis, and can ensure the accuracy of laboratory test results.

Study on the Combination of SOM and K-Means Algorithms in Manufacturing Process Quality Control

2013 ◽  
Vol 427-429 ◽  
pp. 1315-1318 ◽  
Author(s):  
Yi Bing Li ◽  
Fei Pan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Quality Control ◽  
Manufacturing Process ◽  
Low Cost ◽  
Research Topic ◽  
High Quality ◽  
Neural Network Algorithm ◽  
Som Neural Network

Nowadays, customers are seeking products of high quality and low cost. The use of neural networks in quality control has been a popular research topic over the last decade. An adaptive self-organizing mapping (SOM) neural network algorithm is proposed to overcome the shortages of traditional neural networks in this paper. In order to improve the classification effectiveness of SOM neural network, this paper designs an improved SOM neural network, which combined the SOM and K-means algorithms. The flow of combination of SOM and K-means algorithms was analyzed in this paper. And the case study of cement slide shoe bearing in manufacturing process was also given to illustrate the feasible and effective.

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