Artificial Intelligence in Wood Science and Engineering: From Characterization to Predictive Materials Design

Abstract

Artificial intelligence (AI), which includes machine learning, deep learning, and newer methods like multimodal fusion, explainable AI, and generative models have quickly become a disruptive technology throughout the wood value chain. This narrative review explores the changing functions of AI in wood science and engineering, its use from basic material characterization to sophisticated predictive materials design. The review is a synthesis of literature on AI-based species identification, non-destructive anatomy, quantitative anatomy, defect monitoring, property prediction, process optimization, and optimization of engineered wood products including CLT, LVL and particleboards. Literature was sourced from reputable databases, including Scopus, Web of Science and Google Scholar. It considers fundamental data modalities (imaging, spectroscopy, acoustics), modeling approaches, and critically evaluates the move towards passive prediction to interpretable senses and materials design. Results show that AI has already provided significant benefits in accuracy, speed, and objectivity by shifting wood science, based on expert reliance, destructive and empirical approaches, to one that is data-centric, scalable and creative. Nevertheless, the combination of physics-informed and generative models has a high potential to create sustainable and high-performance bio-based materials despite difficulties related to heterogeneity of data, its generalization, and interpretation. Despite limitations in scalable scientific understanding and regulatory acceptability, this review highlights the important ramifications of AI in sustainable materials engineering. AI can make wood one of the most efficient resources in the circular bioeconomy, minimize waste, and facilitate functional innovations, thereby becoming a valuable tool in achieving global sustainability objectives.