Fabric Inspection

Intelligent Automation for fabric cutting

In order to cut fabric such as leather, synthetic or natural cloth efficiently, it's important to recognize any imperfections in the pattern of fabric and to work around them when cutting patterns for clothes, upholstery, furniture and airbags.

BrainMatter can automatically detect imperfections and their severity to determine how to cut fabric, as well as deciding which pieces should be used for each part of the pattern cut.

Challenges

  • Reviewing fabric manually is time-consuming, mind-numbing and inconsistent
  • Natural fabrics such as leather require significant zoom in to accurately recognize  imperfections
  • Recognizing enough imperfection classes to let Inspectors focus on the exceptional cases
person inspecting leather

Key benefits

  • Increasing profitability by hugely improving the amount of fabric used
  • Reducing waste and increasing sustainability 
  • Easy integration into existing cutting equipment camera’s and supporting hardware
  • Detecting a high number of imperfection classes and the ability to project them on a piece of fabric
  • Collecting huge amounts of data about imperfections across fabrics and providing better and more efficient nesting as a service to clients

Perceive

  • Fabric Inspectors view and curate examples of imperfections
  • BrainMatter views imperfections on the fabric
  • Fabric Inspector reviews the new data

Decide

  • BrainMatter learns to interpret imperfections such as bites or tears in leather and judges their severity
  • BrainMatter localizes these imperfections on the image for the cutting process
  • BrainMatter interprets anomalies and lets a human Inspector have the final say
Leather cutting 1920x700

Respond

  • Fabric Inspector defines the rules
  • BrainMatter initiates actions
  • Evaluate actions and improve over time

Functionality

  • Acquiring data from camera’s and distributing captured data for annotations
  • Video and image analysis by the domain expert, leveraging model-assisted labeling
  • Leveraging AI trained models for real-time imperfection detection of image position, class and severity to create a cutting process at the edge
  • Deploying the inference within existing/new camera hardware for cutting machines
  • Expanding and scaling intelligent automation throughout the organization
  • Continuous improvement with a feedback loop

KPI's

Significantly reducing the average time to inspect fabric

Reducing waste by optimising the use of fabrics

Increasing margins by using fabric more effectively

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The intelligent automation platform for visual asset monitoring and inspection.

Asset-intensive industries require an integrated physical and digital view of assets, equipment, buildings and processes. BrainMatter provides a platform for the intelligent automation of tasks that follows a perceive - decide - respond pattern.

Request a demo and find out how to improve the quality of life and work by offloading repetitive & time intensive tasks to machines.

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