Woodspect
Multi-sensor Quality Control
system based on
machine learning algorithms
(Deep Learning AI)
- About
- Advantages
- Defects detected
- Technical Data
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Woodspect is a state-of-the-art, fully automated machine vision system based on neural networks, dedicated to manufacturers and factories of different wood-based products, i.e. furniture, wood flooring, windows, doors, or facades.
Woodspect combines innovative optics with advanced automation and factory IT. By using machine learning, Woodspect is able to replace manual inspection of products, increasing the efficiency of inspection. The system helps automate quality control processes on production lines from raw material to finished board.
The flexibility of the Woodspect systems allows it to be applied to the needs of different types of wood-based products, including but not limited to:
- lamels/lamellas, laths, planks
- boards (including glued laminated wood)
- plywood
- floor panels
The algorithm for detecting defects in wood products based on neural networks achieves the efficiency accuracy of 98%, with a low number of samples classified incorrectly.
Our systems are tailored to corporate requirements, in terms of IT systems: data exchange, user rights management, reporting.
Woodspect combines innovative optics with advanced automation and factory IT. By using machine learning, Woodspect is able to replace manual inspection of products, increasing the efficiency of inspection. The system helps automate quality control processes on production lines from raw material to finished board.
The flexibility of the Woodspect systems allows it to be applied to the needs of different types of wood-based products, including but not limited to:
- lamels/lamellas, laths, planks
- boards (including glued laminated wood)
- plywood
- floor panels
The algorithm for detecting defects in wood products based on neural networks achieves the efficiency accuracy of 98%, with a low number of samples classified incorrectly.
Our systems are tailored to corporate requirements, in terms of IT systems: data exchange, user rights management, reporting.
Advantages:
- wood defect classification accuracy of 98% (compared to 90% accuracy of manual classification)
- user-friendly interface allowing visualization of results in graphical form
- easy integration of the system into the existing production line
- centralized qualitative and quantitative product control
Options:
- preview of quality control statistics for each product with visualization of rejected products
- access to in-depth quality control statistics by time, product, shift, lumber supplier
- defined trend values and alarms
- detailed reporting
- mobile system management module
What do you gain?
- guaranteed consumer satisfaction
- solving labor shortages by introducing automated machine vision based on artificial intelligence in place of manual inspection
- strengthening brand reputation and trust - consistently delivering quality products
- avoiding problems and rising costs in the downstream woodworking process (log > lamella > press > machining > varnishing > packaging > finished product > customer return / lost customer)
- wood defect classification accuracy of 98% (compared to 90% accuracy of manual classification)
- user-friendly interface allowing visualization of results in graphical form
- easy integration of the system into the existing production line
- centralized qualitative and quantitative product control
Options:
- preview of quality control statistics for each product with visualization of rejected products
- access to in-depth quality control statistics by time, product, shift, lumber supplier
- defined trend values and alarms
- detailed reporting
- mobile system management module
What do you gain?
- guaranteed consumer satisfaction
- solving labor shortages by introducing automated machine vision based on artificial intelligence in place of manual inspection
- strengthening brand reputation and trust - consistently delivering quality products
- avoiding problems and rising costs in the downstream woodworking process (log > lamella > press > machining > varnishing > packaging > finished product > customer return / lost customer)
KSM Vision AI-driven Woodspect can distinguish a crack from a saw mark, or a resin pocket - from resin overgrowth.
Woodspect systems provide full detection of the the natural wood-based products on the sides and on the surface of products, including:
- cracks
- resin pockets
- rotten, cracked or falling knots
- mechanical damage (including cavities)
- discoloration (blue stains)
- defects in geometry
Woodspect systems provide full detection of the the natural wood-based products on the sides and on the surface of products, including:
- cracks
- resin pockets
- rotten, cracked or falling knots
- mechanical damage (including cavities)
- discoloration (blue stains)
- defects in geometry
Woodspect is based on two measurement technologies: a 3D scanner, based on the laser triangulation method, and a color scanner using an RGB linear camera.
Woodspect offers two groups of applications, which can be distinguished according to the measurement method:
- Detection of defects on raw boards - algorithms based on neural networks make it possible to detect defects such as resin pockets, broken knots, mechanical damage. This type of quality control can be used i.a. to eliminate defects at lamella mandrel joints and automate the repair of the top surface of products.
- Precise measurement of 3D geometry - by using the laser triangulation method, it enables accurate measurement of the surface of boards after mechanical processing. This type of quality control can be used, i.a. to detect the volume of defects, which enables automation of the puttying process.
Configurable interface and database
Based on the production specification system interface can be adjusted, including automatic system set-up with data upload by higher-order system (e.g. MES) and additional data upload by line operator. Data collected by the system can be automatically uploaded to higher-order system.
Woodspect offers two groups of applications, which can be distinguished according to the measurement method:
- Detection of defects on raw boards - algorithms based on neural networks make it possible to detect defects such as resin pockets, broken knots, mechanical damage. This type of quality control can be used i.a. to eliminate defects at lamella mandrel joints and automate the repair of the top surface of products.
- Precise measurement of 3D geometry - by using the laser triangulation method, it enables accurate measurement of the surface of boards after mechanical processing. This type of quality control can be used, i.a. to detect the volume of defects, which enables automation of the puttying process.
Configurable interface and database
Based on the production specification system interface can be adjusted, including automatic system set-up with data upload by higher-order system (e.g. MES) and additional data upload by line operator. Data collected by the system can be automatically uploaded to higher-order system.
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