The wood industry demands advanced methods for material classification and workpiece characteristic modelling to enhance process monitoring and adaptive process control. This paper presents a sensor fusion approach that integrates data from acoustic emissions, airborne sound, and power consumption during the milling of solid wood and wood-based composites. The aims are to achieve accurate material classification and to model workpiece characteristics such as surface roughness or density. A design matrix was generated by extracting relevant features from the multimodal signals to serve as an input for the classification and regression algorithms. The tested classification approaches to differentiate between workpiece type demonstrated high precision with an average validation accuracy of 92.16 %. Regression models for predicting the surface roughness showed ${\mathrm{R}}^2$ values between 0.79 and 0.97. The density could be predicted with R² values between 0.84 and 0.98. As a conclusion, workpiece types could be classified and important workpiece properties during machining, such as surface roughness and density, could be well described by using information from multiple sensors during machining.

木材工业需要先进的材料分类和工件特征建模方法，以增强过程监控和自适应过程控制。本文提出了一种传感器融合方法，该方法集成了实木和木质复合材料铣削过程中的声发射、空气传播声音和功耗数据。目的是实现准确的材料分类并对工件特征（例如表面粗糙度或密度）进行建模。通过从多模态信号中提取相关特征来生成设计矩阵，作为分类和回归算法的输入。经测试的区分工件类型的分类方法显示出较高的精度，平均验证准确度为 92.16%。${\mathrm{右}}^2$值在 0.79 和 0.97 之间。^{可以用 0.84 和 0.98 之间的 R 2}值来预测密度。总之，可以对工件类型进行分类，并且可以通过在加工过程中使用来自多个传感器的信息来很好地描述加工过程中的重要工件属性，例如表面粗糙度和密度。