Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection

Data-driven models were developed for monitoring the power consumption and wood quality during the lumber manufacturing process. The study proposes hybrid models using vibration signals combined with self-organizing maps (SOMs) for cutting power and waviness prediction in the circular sawing process of Douglas-fir wood under very high feed speed conditions. The acquired vibration signals were fed into SOMs with different topologies for data mapping and automatic sensory feature selection, which were fed into the adaptive neuro-fuzzy inference system (ANFIS) or multilayer perceptron (MLP) neural network (NN) for cutting power and waviness prediction. The monitoring performance of the hybrid SOM-MLP NN and SOM-ANFIS models was compared. The frequency response of vibration signals and its correlation with the cutting parameters as well as the cutting power and waviness were discussed. The study shows that the developed SOM models to select the optimal features from the vibration signals could accurately predict cutting power and waviness when combined with a machine learning model. The prediction performance is highly dependent on the optimal choice of SOM topology. Having a poor choice of SOM topology, fine-tuning the architecture of ANFIS and MLP NN is crucial and greatly impacts the monitoring performance. Based on the obtained results, SOM is recommended for the automatic feature in the machining or tool condition monitoring, where due to process high variability, manual feature selection is a challenging task. SOM combined with an ANN or ANFIS makes a powerful intelligent model for monitoring complex processes such as wood circular sawing.