Julie Cool

Associate Professor

Relevant Degree Programs


Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - April 2022)
Wood sawing monitoring: sensory and artificial intelligence approaches (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.

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Master's Student Supervision (2010 - 2021)
Using x-ray scanned reconstructed logs to predict knot characteristics and log value (2021)

Amabilis fir and western hemlock are an important softwood lumber resource in British Columbia (BC). For several reasons, the available wood volume in BC has consistently decreased since 1990. This decrease suggests that sawmilling processes should become more efficient in utilizing wood. In lumber manufacturing, considering knot characteristics and distribution within lumber pieces in the optimisation of the primary breakdown patterns would significantly impact the product quality or grade. The goal of the project was therefore to investigate the impact of growth history on knot characteristics and how they, in turn, influence the manufacturing of lumber in Coastal and Interior sawmills and build predictive models. Computed tomography (CT) scanning can non-destructively detect knots in wood and is gaining acceptance in the wood industry. Seventy-two amabilis fir and western hemlock trees from three plots located on Vancouver Island, BC were scanned, and images were processed to extract knot characteristics and distribution to reconstruct three-dimensional (3D) log models. The effects of three diameter at breast height (DBH) classes (30, 40 and 50 cm) and three sites on knot characteristics, including knot volume, number of knots, average knot area on CT image sections and knot/tree volume ratio, were investigated. As expected, the knot characteristics of both species increased with the DBH. The difference of knot distribution between amabilis fir and western hemlock suggests that the latter is more sensitive to growth conditions of temperature, precipitation and sunlight. The 3D log models were then processed in Optitek to simulate the sawmill production and assess the impact of the DBH classes and sites (including knots) on the lumber and value recovery in Coastal and Interior sawmills under normal, optimistic, and pessimistic economic cycles. The sawmilling simulations revealed that the Coastal mill produced a lower lumber volume but a higher value due to the type of products manufactured and the primary breakdown patterns being used. The sawmilling simulation results were compared and used in predicting the value of standing amabilis fir and western hemlock trees. Models were developed based on the knot characteristic and tree features to predict the value of a standing tree.

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