Real-time Filtration Automation using CNN for Bio-Factories

Our developed factory automation software is based on the technology of real-time image recognition, utilizing CNN (Convolutional Neural Network) to perform binary classification of the internal filtration stages within the bio-factory's filter dryer. This software plays a crucial role in the factory operation, accurately determining the filtration stages of the filter dryer with high precision through image recognition.

The software is equipped with a feature to recognize real-time video frames and store the assessment results in individual tags. These tags are then utilized in the automation logic, efficiently leading the factory operation towards automation. Operators can leverage this technology to monitor the smooth progress of the internal filtration stages in the filter dryer in real-time, allowing them to take prompt actions when necessary. Additionally, the automated system is expected to contribute significantly to improved productivity and reduced labor costs by eliminating unnecessary manual intervention.

Our IPOES solution combines modern technology with sophisticated image recognition algorithms, providing outstanding performance and accuracy. As a result, the production processes in bio-factories become more stable and efficient, ensuring smooth operations and increased productivity.
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Expected Benefits

Here are two advantages that can be obtained when the CNN replaces the manual visual inspection tasks previously performed by human operators and automates the process:

• Increased Efficiency
  : By automating the process with CNN, the inspection tasks can be performed at a faster and more consistent rate compared to manual human inspection. This leads to improved efficiency in the overall workflow, allowing for higher production output and reduced processing time.
• Cost Reduction
  : By replacing human operators with CNN-based automation, there is a potential for cost savings in terms of labor expenses. The system can operate continuously without the need for breaks or shifts, leading to reduced labor costs and increased cost-effectiveness in the long run. Additionally, the automated system can help minimize product defects and waste, resulting in cost savings associated with rework or product rejection.

System Structure

Demo Video