Improve quality and decrease wood inspection costs with advanced machine vision
Inspecting wood and wooden elements is a difficult task when done manually. Wood is organic and all items are basically unique. This results in high costs and variations in the inspection results.
Manual inspection of aesthetic qualities on products in the wood industry will typically capture about 70-80 percent of the defects. The problem can be both that items with defects slip through the control and that the human eye incorrectly discards items that are within the set standard.
Machine vision can be used to optimize a number of processes in the wood industry – from sawing the log to quality inspection of the final product.
wood inspection
98% accuracy in wood inspection with advanced machine vision and machine learning
JLI vision specializes in automating challenging inspection tasks, the type of inspection that would normally require a highly trained human eye.
By combining 2D and 3D machine vision with machine learning, we can automate the inspection of items that would otherwise require the assessment of experienced employees.
For example, when using wooden boards for quality furniture, it’s very important to detect the knots that could cause holes or dropouts, before the board is processed in expensive operations. Some resin pockets can cause problems, as the resin will get more fluent, if the furniture is placed in warm surroundings. It is very difficult to distinguish between acceptable resin pockets and rejectable resin pockets.
To solve this task, JLI vision has combined machine vision and machine learning. We have trained a neural network with 30,000 images to determine which knots and resin pockets can pass a quality control.
ML wood inspection
Another example is inspection of lacquered surfaces of wooden elements. With a combination of 2D machine vision, multiple cameras, and machine learning, we detect a wide range of different types of defects, e.g. holes in the surface, edge and corner damage, dust in the lacquer, glossy stripes, orange peel, etc.
This built-up knowledge of inspection in the wood industry is the foundation of the new solutions we make. We have documented success with 98% accuracy in detecting defects in challenging inspection tasks.
Case story:
“Impressive ROI”: 98% accuracy in challenging inspection in the wood industry
Machine learning for wood inspection
Read how JLI vision has helped one of the world’s largest manufacturers of furniture automate a challenging aesthetic inspection and increase quality significantly.
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3 reasons to use advanced machine vision in wood inspection
Consistency in quality
By automating the inspection, you free yourself from the lack of reliability and predictability associated with human inspection. Your machine vision system detects the same way every time and does not get tired.
Reliability and scalability
With an automated inspection, you can easily scale your production and maintain the same high level of quality control.
Improved profitability
By reducing the ongoing costs of inspection and optimizing production – for example, by reducing waste and improving quality – you can improve your profitability.
