Friday, August 1, 2025
Plywood manufacturers, especially those operating high-volume mills, face significant challenges in maintaining product quality and maximising yield. Every year, even small defects in veneer can cost mills millions of euros due to wasted resources, costly rework, and product downgrades. But what if there was a way to detect those flaws early and ensure they never make it to the final product? With the introduction of AI-powered defect detection systems, mills can identify and address imperfections like harvester marks, birch bark inclusions, and knots at every stage of production, from peeling to drying to compositing. The result? Raute Corporation has taken the initiative to make the readers understand improved efficiency, substantial cost savings, and a quicker return on investment (ROI).
Recognising the value of high-end defect identification
AI-powered analysers are revolutionising how veneer defects are detected and managed. Rather than relying on traditional methods that might miss subtle imperfections, these advanced systems can spot hidden defects with precision. This allows operators to address quality issues before they cascade down the production line, reducing the risk of defects making it into the final product and causing costly rework or wasted materials.
According to Tuukka Sorvari, Sales Manager of Analyzers and Machine Vision at Raute, one of the most expensive stages in plywood production is drying, which can cost between €1 million and €3 million annually in energy and labor. Sending flawed veneer through the drying process not only wastes that investment but also increases the risk of downgrades and waste. By integrating AI-powered defect detection, mills can ensure only quality sheets make it through the dryer, optimizing both time and resources.
How AI boosts ROI and yield
AI analysers are game-changers when it comes to improving the yield in plywood production. These systems provide real-time quality maps that track moisture content, strength properties, roughness, and waviness of the veneer throughout the process. This early assessment allows operators to make informed decisions about pitch angle, BUR (Back-Up Roll) pressure, and raw material quality before flaws manifest downstream.
Not only do AI analysers identify defects more accurately than legacy systems, but they also help mills adjust their processes in real-time to maintain optimal quality. For example, bark inclusions and sound knots that were previously difficult to detect now show up consistently, ensuring precise veneer control. This improved process visibility creates continuous feedback loops that isolate where quality loss is occurring, enabling operators to address issues before they impact the final product.
As a result, AI-powered defect detection systems reduce the need for rework, increase the throughput of saleable veneer per shift, and accelerate ROI. The result is a more efficient, cost-effective operation that delivers higher-quality products at a faster pace.
Addressing hidden defects early
One common issue in plywood production that often goes undetected until the final sanding stage is veneer roughness. In some mills, up to 15% of panels are downgraded because of excessive roughness, costing significant amounts of money. Traditional defect detection systems typically miss these surface issues, leaving them to be discovered only at the end of the production line, where they are harder and more costly to fix.
With AI-driven systems like Raute’s R7 analyzers, roughness can be tracked continuously throughout the production line. These systems generate a roughness index that enables operators to identify when and where quality begins to deteriorate. By catching this issue early, mills can prevent unnecessary downgrades, reduce sanding time, and ultimately save on costs.
Moreover, the R7 analysers can simulate veneer recovery rates before the drying process. By combining defect maps with moisture and strength data, these systems predict the yield outcome in real-time. Low-recovery sheets can be diverted to green composing units, optimizing dryer loads and preventing the drying process from locking in defects.
Roadmap for immediate action in defect detection
To take full advantage of AI-driven defect detection and achieve long-term performance improvements, manufacturers need to adopt a structured approach. The following strategies can help unlock continuous yield optimisation, future-proof operations, and enhance quality control across the production line:
Data fusion and process simulation: Combining defect detection with data on moisture, strength, waviness, and roughness allows mills to simulate recovery rates and predict drying outcomes. This enables operators to divert low-yield sheets early, saving energy and reducing waste.
Veneer flow control: AI analyzers provide operators with greater control over production flow. By giving real-time feedback on sheet suitability for each phase of the production process, these systems optimize decision-making and improve detection precision, particularly when raw materials vary.
Optimised process control: By equipping operators with intuitive dashboards that track peeling parameters and correlate them with quality outcomes, AI analyzers help automate routine adjustments. This reduces human error, boosts process accuracy, and builds confidence in the workforce.
Continuous model refinement: AI systems can evolve based on the specific needs of the mill, ensuring that defect detection remains accurate even as challenges become more complex.
Stage-gated analytics: AI analysers can be installed at multiple stages throughout the production line, including peeling, drying, and composing. This continuous feedback helps pinpoint defects that occur at any stage, allowing for precise corrections and greater overall quality control.
Why now is the time to taken actions
In today’s highly competitive market, the demand for sustainable production methods, customisation, and strict adherence to carbon regulations is higher than ever. Mills that delay the adoption of AI-powered defect detection systems risk falling behind competitors who have already implemented smarter, more efficient production methods.
As Sorvari points out, every undetected defect represents a lost margin and a missed opportunity to optimise energy usage and resource management. To stay competitive and compliant with rising environmental regulations, plywood manufacturers must integrate AI-powered defect detection into their operations. The sooner mills adopt these technologies, the faster they can realize the benefits of reduced waste, improved quality, and enhanced profitability.
By investing in AI-driven defect detection, plywood manufacturers can protect their bottom line, enhance product quality, and stay ahead of the curve in a rapidly evolving industry. The time to act is now—embrace the future of manufacturing today.
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Tags: AI analyzers, AI defect detection, machine vision, plywood industry, Plywood production, plywood yield, process control, Production Optimization, Raute Corporation, veneer defects, veneer roughness
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