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Related Concept Videos

Interpreting Run Charts01:25

Interpreting Run Charts

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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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The R Chart01:02

The R Chart

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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
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Interpreting X̄ Charts01:13

Interpreting X̄ Charts

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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
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Run Charts01:12

Run Charts

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Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
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The X̄ Chart00:58

The X̄ Chart

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The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Control chart patterns recognition using ANFIS with new training algorithm and intelligent utilization of shape and

Abdol Aziz Kalteh1, Sajjad Babouei1

  • 1Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran.

ISA Transactions
|December 19, 2019
PubMed
Summary

This study introduces a novel method for recognizing nine control chart patterns (CCPs) using shape and statistical features with an optimized fuzzy system. The technique achieves 99.77% accuracy, outperforming existing methods.

Keywords:
ANFISCCPCWOAFeature extractionTraining algorithm

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Area of Science:

  • Industrial Engineering
  • Artificial Intelligence
  • Statistical Process Control

Background:

  • Control Chart Patterns (CCPs) are crucial for monitoring industrial processes.
  • Accurate recognition of CCPs is essential for effective process control.
  • Existing methods may lack robustness or comprehensive pattern recognition capabilities.

Purpose of the Study:

  • To develop a novel, accurate, and robust method for recognizing nine types of CCPs.
  • To leverage intelligent feature extraction and optimized fuzzy systems for CCP recognition.
  • To improve the performance of pattern recognition in statistical process control.

Main Methods:

  • A three-level separation technique for pattern classification.
  • Utilizing a combination of shape and statistical features.
  • Employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) as a classifier.
  • Training the ANFIS using the Chaotic Whale Optimization Algorithm (CWOA).

Main Results:

  • The proposed method achieved a high accuracy of 99.77% in recognizing nine CCPs.
  • Demonstrated superior performance compared to other similar CCP recognition methods.
  • The intelligent feature utilization and optimized ANFIS enhanced recognition robustness.

Conclusions:

  • The developed method offers a significant advancement in CCP recognition.
  • The integration of CWOA-trained ANFIS provides an effective approach for complex pattern recognition.
  • This technique holds promise for improving quality control in industrial settings.