Quality assurance through defect detection in solar cell production



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.

Mor information about the showcase

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  • Automated inspection and defect detection in solar production.



  • Image recognition
  • Position verification
  • Data matching



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