Relevant Degree Programs
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - Nov 2020)
This thesis aimed to develop expert models for intelligent monitoring of the circular sawing process. Circular sawing experiments were conducted under different cutting conditions in kiln-dried, green, and frozen wood to study cutting power and waviness. The effect of the cutting factors and wood conditions on the response variables were reported and discussed. In parallel, the process was monitored using sound, acoustic emission (AE), and vibration sensors. A new wavelet-based methodology was developed to enable sound signal monitoring in very noisy environments by identifying and conserving the sound components corresponding to the sawing process. Emphasis was then put on sensory feature selection of AE signals in time and frequency domains. Feature selection was optimised by connecting the decision-making model with a heuristic optimisation algorithm to maximise the monitoring performance. Accordingly, particle swarm optimisation was linked with a neuro-fuzzy model. To eliminate the need for sophisticated signal processing, an automatic feature selection process was studied using the vibration signals. For this purpose, segments of the signals were fed into a self-organizing map model combined with the neuro-fuzzy and multilayer perceptron neural network models. The results showed that sawing frozen wood requires more cutting power than dry and green wood. However, freezing conditions lowered the waviness indicating a reduced sawing deviation. It was shown that the proposed wavelet-based approach for sound signal monitoring could be effective in noisy applications. Optimal feature selection could increase the monitoring accuracy when using an AE sensor. Interestingly, the automatic feature selection resulted in the highest accuracy indicating that combining the self-organizing map with an intelligent decision-making model could be used in sawmilling applications using a vibration sensor. In general, the results of this study proposed expert models for online monitoring of cutting power and waviness, which is of great importance in transitioning towards smart lumber manufacturing.