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Related Concept Videos

Quality Control01:05

Quality Control

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.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality Assurance01:19

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
Glassware Calibration01:11

Glassware Calibration

Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...

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Related Experiment Video

Updated: Jun 16, 2026

Intermediate Strain Rate Material Characterization with Digital Image Correlation
07:59

Intermediate Strain Rate Material Characterization with Digital Image Correlation

Published on: March 1, 2019

Quality control of small mechanical pieces using optical correlation techniques.

G Indebetouw, T Tschudi, G Herziger

    Applied Optics
    |February 19, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a holographic matched filtering technique for precise contour shape control of micro-mechanical elements. Computer-generated holograms optimize spatial filters, enabling micron-level shape error detection for quality assurance.

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    Last Updated: Jun 16, 2026

    Intermediate Strain Rate Material Characterization with Digital Image Correlation
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    Intermediate Strain Rate Material Characterization with Digital Image Correlation

    Published on: March 1, 2019

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
    05:47

    Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

    Published on: August 29, 2025

    Area of Science:

    • Optics and Photonics
    • Mechanical Engineering
    • Metrology

    Background:

    • Precise control of micro-mechanical element contours is crucial for advanced manufacturing.
    • Existing metrology techniques may lack the resolution or efficiency for sub-micron defect detection.
    • Holographic techniques offer potential for high-precision optical measurements.

    Purpose of the Study:

    • To develop and demonstrate a holographic matched filtering technique for controlling the contour shape of small mechanical elements.
    • To optimize spatial filters using computer holograms for enhanced measurement accuracy.
    • To establish a method for calibrating optical correlator output based on shape error.

    Main Methods:

    • A holographic matched filtering approach was employed.
    • Spatial filters were optimized using computer-generated holograms.
    • An optical correlator was calibrated using a well-defined response curve to quantify shape error.

    Main Results:

    • The technique successfully controlled the contour shape of small mechanical elements.
    • Optimization of spatial filters yielded a precise response curve for calibration.
    • Shape errors on the order of a few microns (less than 1% of the controlled dimension) were detected.

    Conclusions:

    • Holographic matched filtering is an effective method for micro-mechanical contour control.
    • Computer hologram techniques provide extensive possibilities for optimizing spatial filters.
    • The developed technique offers high sensitivity for detecting minute shape deviations in manufacturing.