40 | Panel Perspectives: Fraunhofer perspective on AI
INTELLIGENCE PATH AI: THE
Artificial Intelligence (AI) in the wood-based panel industry is growing in momentum. Constanze Hasterok and Henrike Stephani of German research organization Fraunhofer provide an applied research perspective
Above: Only AI made it possible to automatically inspect products with highly variant background structures W
ood-based panels sit at the heart of modern construction and furniture. Their production is complex: natural feedstock varies, processes are thermomechanically coupled, quality targets are strict, and sustainability demands are growing.
An increasing amount of data is
being collected to monitor and optimize production processes. AI has emerged as the driving force that turns this data into a measurable value. Fraunhofer, as the German applied research Institution, leverages AI to ensure consistent quality, increase yield, and cut energy use towards more sustainability. AI is applied to detect anomalies in the product design, the production process, as well as to predict and inspect product quality.
Microstructural analysis, simulation, and geometric modeling are essential tools for material design, to develop new types of panels and the optimization of their performance under certain conditions. One of the leading applications of AI is anomaly detection in the production process,
where AI-driven systems continuously monitor sensor data, such as pressure and temperature, to identify deviations in real time. The enormous volume of sensor data collected across the entire plant presents a significant challenge. It is essential to take into account the often-complex correlations among hundreds of individual sensor measurements. AI can simplify this complexity, allowing operators to intuitively and reliably understand the current state of the plant at a glance. In the event of anomalies, they are guided towards root cause and can respond swiftly to unexpected process deviations, helping to reduce waste and energy consumption. Quality inspection of both raw materials and the final product is another vital application of AI. Laboratory-based monitoring of raw materials, as commonly practiced in wood-based panel production, often suffers from delays, resulting in uncertainty about product quality. Subsequent deviations in quality are only detected late in the process, commonly resulting in scrap or wasted raw materials.
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AI-based assistance systems can eliminate these inefficiencies, leading to significant, measurable annual cost savings. Furthermore, wood species identification through hyperspectral imaging is gaining traction. AI algorithms interpret complex spectral data to distinguish between wood types, optimizing raw material use and supporting traceability as an essential component of sustainable production.
AI-based visual inspection monitors and ensures the quality of the final product. Since defects usually need to be classified, anomaly detection is paired with classification. These systems can fully automate the sorting of the final product according to its quality. Although surface inspection systems have existed for a long time, only AI made it possible to automatically inspect products with highly variant background structures. In the past, wood panels could not be effectively monitored without human intervention. Thanks to advances in AI, that is no longer the case.
Finally, large language models (LLMs) can support plant operators and
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