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AI-Powered Visual Inspection System for Manufacturing Quality Control
  1. case
  2. AI-Powered Visual Inspection System for Manufacturing Quality Control

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AI-Powered Visual Inspection System for Manufacturing Quality Control

dac.digital
Manufacturing

Challenges with Quality Control in Wood Furniture Production

Prohan Wood Furniture faced significant challenges in maintaining quality during the production of wooden furniture. Traditional manual inspections were insufficient to detect subtle defects like gaps, cracks, and splits in glued wood panels, especially given the high production speeds (up to 30 m/s). These undetected defects led to production inefficiencies, increased product rejection rates, customer dissatisfaction, and financial losses due to returns and waste.

About the Client

Prohan is a manufacturer specializing in high-end wooden furniture made from hardwoods. They prioritize quality and precision due to the cost and difficulty of working with these materials.

Project Goals

  • Automate quality inspection of wood panels using computer vision.
  • Reduce the number of defective products reaching customers.
  • Increase defect detection rates to improve production efficiency.
  • Minimize waste of materials during the manufacturing process.
  • Enable remote monitoring and configuration of the quality inspection system.

System Functionality

  • Real-time defect detection.
  • Automated alerts for detected defects.
  • Data logging and reporting of inspection results.
  • Remote monitoring and configuration.
  • Scalable to handle varying production volumes.
  • Adaptable to different types of wood and panel sizes.

Technology Preferences

Computer Vision
Machine Learning (PyTorch)
Cloud Computing (for remote access and configuration)
Python
OpenCV

Required Integrations

  • Existing production line sensors (for triggering inspections)
  • Potential integration with ERP/CRM systems for quality data analysis and reporting

Non-Functional Requirements

  • High accuracy in defect detection (target: 90% reduction in defects)
  • Low latency to avoid slowing down the production line.
  • Scalability to support future production growth.
  • Robustness to operate in a dusty and low-light manufacturing environment.
  • Secure data storage and access control.

Expected Business Impact

The implementation of this system is expected to significantly improve Prohan's bottom line by reducing product returns, minimizing waste, increasing production efficiency, and improving customer satisfaction. The initial case study demonstrates a 90% reduction in defects and zero customer returns related to these flaws. This translates to substantial cost savings and a strengthened brand reputation.

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