Quality assurance through defect detection in solar cell production

 

Challenges

Today, inspections to find faults in photovoltaic panel production are still carried out manually by a worker on site.

In order to simplify this process and to reduce the error rate, elunic was commissioned to develop a system based on artificial intelligence that automatically detects errors.

 

Key Features

  • Increase of quality and reduction of rejects during the production process.
  • Increase of throughput through automation.
  • Reduction of costs.

 

Solutions & Services

  • Use of deep learning algorithms for error detection.
  • Labeling and classifying of custom errors.
  • Installation of required hardware.
  • MES and quality management integration.

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    Benefit

    • Automated inspection and defect detection in solar production.

     

    Data

    • Image recognition
    • Position verification
    • Data matching

     

    Technology

    • AI.SEE
    • AWS IoT Core
    • AWS Kinesis
    • TensorFlow