AI Solution for Quality Control in Wood Panel Production

About the Client:
A large-scale forestry company in Uruguay engaged in the production of wood panels. The company faced challenges related to consistency and accuracy in quality control during the classification of wood panels, which affected both production efficiency and the quality of the final product.

Challenge:
The previous manual quality control system relied on a visual inspection process to classify defects in the wood panels. This process was not only slow but also led to inconsistencies due to factors such as worker fatigue, subjectivity, and variability between different work shifts. The client needed a solution to improve the accuracy and efficiency of the classification process, reducing errors and increasing the precision of defect identification.

Solution Implemented:
As part of a project funded by the National Agency for Research and Innovation (ANII), an integrated solution combining advanced hardware and artificial intelligence software was developed and implemented.

The solution involved the installation of high-precision cameras, lighting systems, and support structures, which capture real-time images as the wood panels move along the conveyor belt. These images are processed by an AI-powered vision system based on deep learning, enabling the automatic detection, segmentation, measurement, and classification of defects.

A data storage and visualization system was also implemented, allowing the plant to monitor quality control results in real time, providing valuable insights into the production process.

Technologies Used:

• Edge Computing
• Databases
• Docker
• Computer Vision
• Deep Learning

Results:

• Improved Consistency: The automated classification system has eliminated variability between shifts and human subjectivity, ensuring uniform quality control throughout production.
• Increased Accuracy: The AI-powered vision system has significantly improved defect detection accuracy compared to manual methods.
• Productivity Optimization: The automation of the quality control process has reduced the time required for inspections, significantly increasing the plant’s overall efficiency.

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