Akridata

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Transform Manufacturing
Assembly Line Inspection

Many manufacturers have deployed vision systems into their manufacturing lines to automate quality inspection throughout the manufacturing process, but struggle to combat false positives and negatives.

 

Data Explorer uses deep learning to classify the images captured by your vision system to minimize the shipment of defective products while maximizing high yields – reducing the impact of defective product, increasing product quality and driving down operational costs.

WHO WE SUPPORT

Medical Devices
Agriculture
Automotive
Rail Transportation

Data Explorer seamlessly interfaces with your in-line inspection system, automating the generation of an autonomous, secondary set of decisions. This supplementary set of decisions serves as a benchmark for comparison with those made by your primary in-line system. With deep learning, you can analyze both the dataset and the model decisions to improve the models used by in-line systems.

  • Pilot/NPI Manufacturing Lines:
    Validate the performance and quality of the decisions made by your in-line system and provide data required for model explainability for Total Product Lifecycle Quality and regulatory requirements
  • Production Manufacturing Lines:
    Have an independent second pass to minimize false positives/negatives to improve yields and provide continuous improvement to your in-line edge models through deep learning

HOW IT WORKS

Minimize False Positives &
Negatives with Data Explorer

1. Customize Feature Representation
Data Explorer connects to the image database used by your current vision system and uses Deep Learning to classify the images captured by your vision system.  The software can be easily customized to match your specific production environment.
2. Organize and Explore
Utilizing clustering techniques, Data Explorer categorized visual data into distinct “pass” and “fail” groups, further segmenting them into detailed subgroups.
3. Analyze and surface production anomalies
This hierarchical approach provided a comprehensive overview of production variations, facilitating the identification of specific failure types and areas of variation.
Overlaying Data Insights:
Overlaying Data Explorer’s findings with existing predictions enabled precise human intervention, refined inspection decisions, and reduced errors in part flagging.

CUSTOMER SPOTLIGHT

This Medical Device Company Decreased False Positives by 40%

A medical device company sought to improve accuracy and efficiency in its computer vision-based inspection lines. Existing computer vision technologies led to frequent false positives and negatives, impacting profits, increasing customer complaints, and inflating operational costs.

The company's in-line inspection system, efficient but inaccurate, fell short despite repeated model tuning. Real-world production conditions and a rigid, speed-focused approach caused incorrect part flagging, posing a challenge in balancing speed without sacrificing accuracy or risking defective product shipments.

This medical device company implemented Akridata to boost accuracy and efficiency in assembly line inspections.

Leveraging Akridata’s Data Explorer, the pharmaceutical company achieved a notable 40% decrease in false positives and uncovered a 30% reduction in false negatives.

This improvement significantly reduced inventory wastage and prevented the shipment of defective products.

The implementation of Akridata’s solution elevated product quality standards, safeguarding the company’s esteemed brand reputation.

Improved operational accuracy efficiency and reduced recalls while cutting costs.

FAQs

Akridata’s Visual Data Copilot uses deep learning to classify images captured by vision systems on manufacturing lines. It minimizes false positives and negatives, helping manufacturers identify defects more accurately, improving product quality, and reducing waste.
Yes, Visual Data Copilot seamlessly integrates with current in-line inspection systems and can be customized to fit specific production requirements. It provides automated decision-making to enhance the efficiency of existing quality control processes.
The solution utilizes advanced clustering and deep learning techniques to organize and classify visual data. By accurately distinguishing between “pass” and “fail” categories, it significantly reduces false positives, preventing unnecessary rework and ensuring only defective products are flagged.
Akridata’s solution is versatile and supports various industries, including medical devices, agriculture, automotive, and rail transportation, offering tailored image analysis to meet the specific quality inspection needs of each sector.
Manufacturers can expect a significant reduction in false positives (up to 40% as highlighted in customer success stories), improved product quality, fewer recalls, and lower operational costs, ultimately boosting overall efficiency.
Ready to experience the power of Akridata Data Explorer?