Skip to content
  • Our Work
    • Fields
      • Cardiology
      • ENT
      • Gastro
      • Orthopedics
      • Ophthalmology
      • Pulmonology
      • Surgical Intelligence
      • Surgical Robotics
      • Urology
      • Other
    • Modalities
      • Endoscopy
      • Medical Segmentation
      • Microscopy
      • Ultrasound
  • Success Stories
  • Insights
    • Magazine
    • Upcoming Events
    • Webinars
    • Meetups
    • News
    • Blog
  • The company
    • About us
    • Careers
Menu
  • Our Work
    • Fields
      • Cardiology
      • ENT
      • Gastro
      • Orthopedics
      • Ophthalmology
      • Pulmonology
      • Surgical Intelligence
      • Surgical Robotics
      • Urology
      • Other
    • Modalities
      • Endoscopy
      • Medical Segmentation
      • Microscopy
      • Ultrasound
  • Success Stories
  • Insights
    • Magazine
    • Upcoming Events
    • Webinars
    • Meetups
    • News
    • Blog
  • The company
    • About us
    • Careers
Contact

Defect Detection in Ceramics

Quality control in the ceramic industry has lately started to reap the benefits of automation. However, quality control is still performed manually in many factories around the world, as a subset of the production batch is inspected by trained personnel for various visual defects such as cracks, granulation and abnormal surface reliefs.  Manual inspection being prone to human error due to subjective reasoning and conditions, this solution is far from optimal. Additionally, when a tile is manufactured in mass production lines, manual inspection becomes a limiting factor to the speed of production. This downside of manual inspection has costly consequences like possible waste of materials, degraded quality of the shipped product and loss of labor time. This justifies a call for automated inspection and defect detection in ceramics.

Defects detection in ceramics

Automated defect detection in ceramics

Automation of the inspection process can significantly improve the quality of a batch and increase tile production rate. Computer vision based system can be mounted to alert for possible damages to a sample of the production batch, and further also serve to examine each single tile. Furthermore, automatic classification of the defects can shed light on possible hardware malfunction and contribute to tracking down defective component. Such vision-based defect detection and classification system requires relatively cheap hardware, that is to say designated cameras and integration in the production pipeline. The software side of the system requires adaptation to the type of material used in the factory, the illumination conditions in the production line and a learning stage for taking into account the types of possible defect.RSIP Vision has constructed for one client a computer vision based algorithm, specifically designed to automatically find flaws in ceramic tiles before mass production. This automated system for defect detection in ceramics employs advanced algorithms that learn the geometrical statistics of the tiles and then determine acceptance and rejection conditions: these machine learning algorithms can detect minute defects and take into account a wide-range of possible defects, including broken corners, spots, low contrast stains, defective printing and more.

Our work resulted in substantial decrease in defective tiles produced, reduction of defective tiles being shipped out, reduced costs to the manufacturer, increased satisfaction through distribution channels and higher profits for our client.

The demand for tailor-made computer vision based algorithms for quality control and inspection is constantly growing, especially for mass-production lines. Defects in manufactured product are inevitable, however their rate and number can be dramatically reduced by automatic inspection and, when needed, timely interruption of defective batches production. To learn more about RSIP Vision’s solutions for quality control please read about our automated optical inspection projects.

Share

Share on linkedin
Share on twitter
Share on facebook

Related Content

Percutaneous Nephrolithotomy

PCNL – Planning and real-time navigation

Prostate Tumor Segmentation

Implementing AI to Improve PI-RADS Scoring

RAS Navigation

Tissue Sparing in Robotic Assisted Orthopedic Surgeries

Procedural Planning in urology

Procedural Planning in Urology

C Arm X-Ray Machine Scanner

Radiation Reduction in Robotic Assisted Surgeries (RAS) Using AI

Visible spectrum color

Hyperspectral Imaging for Robotic Assisted Surgery

Percutaneous Nephrolithotomy

PCNL – Planning and real-time navigation

Prostate Tumor Segmentation

Implementing AI to Improve PI-RADS Scoring

RAS Navigation

Tissue Sparing in Robotic Assisted Orthopedic Surgeries

Procedural Planning in urology

Procedural Planning in Urology

C Arm X-Ray Machine Scanner

Radiation Reduction in Robotic Assisted Surgeries (RAS) Using AI

Visible spectrum color

Hyperspectral Imaging for Robotic Assisted Surgery

Show all

RSIP Vision

Field-tested software solutions and custom R&D, to power your next medical products with innovative AI and image analysis capabilities.

Read more about us

Get in touch

Please fill the following form and our experts will be happy to reply to you soon

Recent News

PR – Intra-op Virtual Measurements in Laparoscopic and Robotic-Assisted Surgeries

PR – Non-Invasive Planning of Coronary Intervention

PR – Bladder Panorama Generator and Sparse Reconstruction Tool

PR – Registration Module for Orthopedic Surgery

All news
Upcoming Events
Stay informed for our next events
Subscribe to Our Magazines

Subscribe now and receive the Computer Vision News Magazine every month to your mailbox

 
Subscribe for free
Follow us
Linkedin Twitter Facebook Youtube

contact@rsipvision.com

Terms of Use

Privacy Policy

© All rights reserved to RSIP Vision 2021

Created by Shmulik

  • Our Work
    • title-1
      • Ophthalmology
      • Uncategorized
      • Ophthalmology
      • Pulmonology
      • Cardiology
      • Orthopedics
    • Title-2
      • Orthopedics
  • Success Stories
  • Insights
  • The company