Quality Control of Labelstock Using Fuzzy Exponentially Weighted Moving Average (FEWMA) Control

Production development in the industrial era sues the company to continue creating the best product for consumer society. Product quality is the main consideration for the consumer in selecting products. This is certainly driving the company to maintain the quality of its products. Therefore, compan...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-03, Vol.1863 (1), p.12038
Main Authors: Pratiwi, R M, Wibawati, Ahsan, M, Mashuri, Khusna, H
Format: Article
Language:English
Subjects:
Ambiguity
Control charts
Control data (computers)
Fuzzy control
Inspection
Physics
Quality control
Thickness
Weight
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Summary:Production development in the industrial era sues the company to continue creating the best product for consumer society. Product quality is the main consideration for the consumer in selecting products. This is certainly driving the company to maintain the quality of its products. Therefore, companies need to control the quality of production so their products can fulfill the standards set. One of the ways that can be used to control quality is using the control chart. The most frequently used control chart is the Shewhart type control chart. However, the Shewhart control chart can only be used for crisp data, while for data containing ambiguity, a fuzzy control chart can be applied. In this research, the thickness of the label stock glue quality on HVS P 60 is monitored using the fuzzy control chart. The type of fuzzy control chart used in this study is the FEWMA (Fuzzy Exponentially Weighted Moving Average) control chart. This control chart was chosen because it can monitor data that contains ambiguity and data with small process shifts. In this research, rigorous inspection (α-cuts) was used to detect shifting data. Based on the analysis, it was found that the weight (λ) 0.1 is the optimum weight value in detecting a process shift in the thickness of the label stock glue. Through the cause and effect diagram, four factors that cause the out of control data was found, namely machine, method, man, and material.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1863/1/012038