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Diaper Impurity Detector: Specifications & Testing Principles

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The Diaper Impurity Detector is a cutting-edge quality control device designed to identify foreign particles, contaminants, or defects in diaper materials such as absorbent cores, topsheets, and backsheets. By leveraging advanced imaging and optical sorting technologies, it ensures product safety and compliance with hygiene standards. Ideal for diaper manufacturers and sanitary product suppliers, this equipment minimizes recall risks and enhances consumer trust by detecting imperfections like fibers, plastic fragments, or chemical residues.


Applicable Standards:

  • ASTM F2388: Standard Test Method for Contamination in Absorbent Hygiene Products.
  • ISO 13628-7: Petroleum and Natural Gas Industries—Detection of Foreign Matter in Disposable Hygiene Products.
  • GB/T 30133: Chinese National Standard for Diaper Quality Inspection.
  • EDANA 40.0-95: European Guideline for Contaminant Detection in Baby Care Products.

Main Parameters (Table 1):


ParameterRange/Specification
Detection Resolution0.01 mm² (minimum detectable particle)
Inspection Speed50–300 m/min (line speed)
Light SourceUV/White LED (adjustable intensity)
Sample Width100–800 mm (web width)
Defect Classification10+ categories (e.g., fibers, metal, plastic)
Image Capture Rate60 fps (full HD)
Rejection Accuracy≥99.5% (defective unit removal)

Working Principle:
The detector uses high-resolution cameras mounted above and below the diaper material web. As the product moves along the production line, the cameras capture images under UV and visible light to highlight contrasts between the base material and contaminants. Machine learning algorithms analyze the images in real-time, flagging anomalies that exceed predefined size or shape thresholds. A pneumatic rejection system automatically ejects defective units from the line.

Test Principle:
The detection process involves:

  1. Illumination: Dual-mode lighting (UV for fluorescent contaminants, white light for visible debris) enhances defect visibility.
  2. Image Acquisition: Line-scan cameras capture 100% of the material surface at 60 frames per second.
  3. Algorithm Analysis:
    • Thresholding: Pixels with intensity deviations >5% from the baseline are marked.
    • Morphology Filtering: Removes noise (e.g., textile texture) to isolate true defects.
    • Classification: Deep learning models categorize defects by type (e.g., Dfiber=0.1–2 mm2, Dplastic>2 mm2).
  4. Rejection: Defective diapers are diverted via air jets within 50 ms of detection.

Applications:

  • Raw Material Inspection: Screening incoming rolls of nonwoven fabric, SAP, or elastic threads.
  • In-Line Quality Control: Detecting contaminants during diaper assembly (e.g., glue spots, broken elastic).
  • Final Product Audit: Ensuring retail-ready packs meet zero-defect tolerances.

By integrating this detector into production workflows, manufacturers achieve up to 99.9% defect-free output, aligning with global safety regulations and reducing liability risks.


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