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Related Concept Videos

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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Interpreting X̄ Charts01:13

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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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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
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In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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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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SU-E-T-207: Flatness and Symmetry Threshold Detection Using Statistical Process Control.

C Able1, C Hampton1, A Baydush1

  • 1Wake Forest School of Medicine, Winston-Salem, NC.

Medical Physics
|May 19, 2017
PubMed
Summary
This summary is machine-generated.

Statistical process control (SPC) using process control charts (PCC) for steering coil currents (SCC) can detect changes in beam uniformity before exceeding AAPM TG-142 criteria. Further research is needed to refine alarm sensitivity and reduce false positives.

Keywords:
Electron beamsLinear acceleratorsPhotonsProcess monitoring and controlServomechanisms

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Quality Assurance

Background:

  • AAPM TG-142 guidelines mandate maintaining beam uniformity (flatness and symmetry) within 1% of baseline.
  • Maintaining consistent beam quality is crucial for accurate radiation therapy delivery.
  • Traditional methods may not always provide early detection of subtle beam uniformity deviations.

Purpose of the Study:

  • To evaluate the efficacy of statistical process control (SPC) using process control charts (PCC) for steering coil currents (SCC).
  • To determine if PCC of SCC can detect changes in beam uniformity before the 1% constancy threshold is breached.

Main Methods:

  • Routine daily warm-up data for transverse and radial plane SCCs were collected.
  • Average and range (Xbar/R) PCC were applied to analyze 6 MV beam data.
  • An experiment involved systematically altering individual SCCs and measuring the impact on beam uniformity using a scanning system.

Main Results:

  • PCC successfully detected SCC changes that led to exceeding the 1% beam uniformity criteria for 3 out of 4 steering coils.
  • The transverse position coil's result was less reliable due to observed SCC drift during the experiment.
  • The study demonstrated the potential sensitivity of PCC for monitoring SCCs.

Conclusions:

  • X-bar/R charts of SCC are effective in identifying significant variations before beam uniformity criteria are violated.
  • High sensitivity of PCC may lead to false alarms, necessitating further investigation.
  • Future studies should explore combining SCC alarms to optimize the false positive rate for beam uniformity interventions.