Faculty profile headshot photo of Julie Cool

Julie Cool

Associate Professor

Forest Sciences Centre 4024
2424, Main Mall
Vancouver, BC V6T 1Z4

work phone: 604-827-0639

My area of interest is wood machining and process optimization in both the primary and secondary wood manufacturing sectors. My overall research objective is to provide sound scientific results using both fundamental and applied research that can be easily translated to the wood industry to increase wood recovery and product quality which directly impacts revenues and local economies.

Therefore, I am motivated by research that focuses on the wood-tool interaction which is critical in understanding how to improve surface quality and product durability, reduce waste, and eliminate unnecessary manufacturing operations. I use an approach that combines modelling and laboratory tests to determine how cutting parameters of different machining processes affect specific performance indicators.

I also believe that it is important to better link forest management and silvicultural practices to the end-user’s needs in order to improve raw material allocations, focus on market-pull operations and foster product innovations and development based on specific wood properties and the corresponding wood processing techniques. As the pressure on forest lands constantly increases, I feel this area of research could benefit both large-scale industries and small rural communities.


Modelling wood fracture mechanics in primary wood products manufacturing Current
June, 2015 – June, 2020

(NSERC Discovery) Primary breakdown of logs having a diameter ranging from 2.5 to 30 inches is often done using a chipper-canter that produces, in a single operation, a cant and chips while minimizing sawdust production. However, the cutting action is quite complex since cutting direction changes along the tool path. As cutting direction depends on cutterhead and log diameters, process optimization has been done empirically for chip and cant surface quality. Therefore, no data is available on the cutting forces and/or the fracture mechanics that govern the wood-tool interaction in chipper-canters. This lack in knowledge could be overcome by using finite element modelling. However, research on finite element modelling of wood machining processes has been focused on orthogonal cutting. These models, based on metal cutting theories, have yielded good correlations but do not take into account fracture mechanics at the cellular level. A hybrid cellular/macroscopic finite element model has been developed to study failure mechanism in orthogonal cutting across the grain. The authors were able to demonstrate the need for both macroscopic and microscopic modelling. My research program focuses on acquiring experimental data that will be used to develop a hybrid finite element model of wood peripheral cutting process. The approach consists in acquiring thorough experimental data on cutting forces, fracture mechanics, chip formation and quality, as well as surface quality. This will give quantitative and qualitative information on cutting dynamics involved in different cutting direction. These findings will be correlated with cutting force, chip quality, and surface quality measurements made in the industry. Second, a hybrid cellular/macroscopic peripheral cutting finite element model will be developed for different cutting direction. This should enhance our knowledge of the cutting dynamics involved in peripheral cutting. Finally, the experimental and industrial data will be used to validate the model. In the long-term, this model will be further adapted to the particular machining operations of chipper-canters so change in cutting direction along the cutting path will be introduced as a function of cutterhead and log diameters. The model will then be used to study the impact of different cutting parameters on cutting dynamics, chip formation, and surface quality of chipper-canters.

Optimizing hem-fir resource transformation based on existing x-ray CT images Current
May, 2018 – April, 2020

The purpose of the project is to quantify the benefits associated with using a fibre attribute-based approach when optimizing resource transformation. More specifically, the project has four objectives: 1) Characterize external and internal wood attributes based on x-ray CT scans, 2) Model a lumber value chain using an industrial software from the partner organization, 3) Quantify the potential economic gains involved in using different fibre attributes when optimizing lumber manufacturing, and 4) Relate growth history with log transformation. Linking forest management approaches with primary wood transformation strategies and the corresponding product basket should provide valuable information to ensure a steady timber supply for a strong and diversified bioeconomy.

Achieving quality control during veneer drying by using big data statistics Completed
June, 2017 – May, 2018

Veneer drying has traditionally been done through the adjustment of process parameters (temperature, feed speed of veneer, moisture content, air flow, etc.) by experienced personnel. Although using a qualitative approach is effective in assessing how any modification in parameters impacts the veneer, it often yields a significant loss in quality. This is due to the delays that are involved in reaching the kiln’s steady state following parameter modification. However, kilns are now being equipped with various sensors that allow the tracking of many parameters related to both the kiln and the veneers.

The industry partner in this project has adopted such technologies and is in the process of implementing an advanced quality control system to predict kiln performance for specific veneer grades. Their goal is to transition from a qualitative approach to one that includes a quantitative one. However, the amount of data being generated is overwhelming, and makes it challenging to identify which parameter has a significant impact and should be carefully monitored by the quality control team.

The research objective is to link raw material characteristics with the veneer drying process. More specifically, we will use big data statistics to identify what process parameter have the most impact on product quality and how should those significant parameters should be controlled.

Selected Publications

V Nasir, M Kooshkbaghi, J Cool, F Sassani (2021). Cutting tool temperature monitoring in circular sawing measurement and sensor fusion-based prediction The International Journal of Advanced Manufacturing Technology
V Nasir, J Cool (2021). Cutting power and surface quality in sawing kiln-dried, green and frozen hem-fir wood Wood Science and Technology 55(2): 505-519
J van Blokland, V Nasir, J Cool, S Avramidis, S Adamopoulos (2021). Machine learning-based prediction of internal checks in weathered thermally modified timber Contruction and Building Materials 281
A Schild, J Cool (2021). Accuracy of visually sorting waste wood - An exploratory study Canadian Journal of Forest Research
Nasir V, Cool J (2020). Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection The International Journal of Advanced Manufacturing Technology 108: 1811-1825
Ayanyele S, Nasir V, Avramidis S, Cool J (2020). Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression European Journal of Wood and Wood Products
Nasir V, Cool J (2020). Characterization, optimization, and acoustic emission monitoring of airborne dust emission for sustainable wood sawing The International Journal of Advanced Manufacturing Technology 109: 2365-2375
Julie Cool, Simon Ellis and Feng Jiang (2020). A Tour of Wood Product Manufacturing Facilities in British Columbia as an Example of Experiential Learning Natural Sciences Education
V Nasir, M Kooshkbaghi, J Cool (2020). Sensor fusion and random forest modeling for identifying frozen and green wood during lumber manufacturing Manufacturing Letters 26: 53-58
V Nasir, J Cool (2020). A review on wood machining: Characterization, optimization and monitoring of the sawing process Wood Material Science and Engineering 15(1): 1-16
S Ahmed, J Cool, ME Karim (2020). Application of decision tree based techniques to veneer processing Journal of Wood Science 66(1): 1-8
V Nasir, J Cool, F Sassani (2019). Intelligent machining monitoring using sound signal processed with the wavelet methods and a self-organizing neural network IEEE Robotics and Automation Letters 4(4): 3449-3456
M Fredriksson, J Cool, S Avramidis (2019). Automatic knot detection in coarse resolution cone-beam computed tomography images of softwood logs Forest Products Journal 69(3): 185-187
W-Y Chang, S Wang, C Gaston, J Cool, H An, BR Thomas (2019). Economic evaluations of employing tree improvement for planted forests: A literature review Bioproducts Business 4(1): 1-14
V Nasir, S Nourian, S Avramidis, J Cool (2019). Classification of thermally treated wood using machine learning techniques Wood Science and Technology 53(1): 275-288
V Nasir, S Nourian, S Avramidis, J Cool (2019). Stress wave evaluation by accelerometer and acoustic emission sensor for thermally modified wood classification using three types of neural networks European Journal of Wood and Wood Products 77(1): 45-55
W-Y Chang, C Gaston, J Cool, and B Thomas (2019). A financial analysis of using improved planting stocks of white spruce and lodgepole pine in Alberta, Canada: Genomic selection versus traditional breeding Forestry: An International Journal of Forest Research 92(3): 297-310
V Nasir, S Nourian, S Avramidis, J Cool (2019). Prediction of physical and mechanical properties of thermally modified wood based on color change evaluated by means of group method of data handling (GMDH) neural network Holzforschung 73(4): 381-392
V Nasir, S Nourian, S Avramidis, J Cool (2019). Stress wave evaluation for predicting the properties of thermally modified wood using neuro-fuzzy and neural network modeling Holzforschung 73(9): 817-838
N Vahid, J Cool, F Sassani (2019). Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection The International Journal of Advanced Manufacturing Technology 102(9-12): 4179-4197
V Nasir, J Cool (2019). Optimal power consumption and surface quality in the circular sawing process of Douglas-fir wood European Journal of Wood and Wood Products 77(4): 609-617
A Schild, J Cool, MC Barbu, G Smith (2019). Feasibility of substituting core layer strands in randomly OSB with contaminated waste wood particles Wood Material & Science Engineering 1-8
V Nasir, S Nourian, Z Zhou, S Rahimi, S Avramidis, J Cool (2019). Classification and characterization of thermally modified timber using visible and near-infrared spectroscopy and artificial neural networks: A comparative study on the performance of different NDE methods and ANNs Wood Science and Technology 53(5): 1093-1109
V Nasir, A Mohammadpanah, J Cool (2018). The effect of rotation speed on the power consumption and cutting accuracy of guided circular saw: Experimental measurement and analysis of saw critical and flutter speeds Wood Material Science and Engineering 1-7
M Fredriksson, J Cool, I Duchesne, D Belley (2017). Knot detection in computed tomography images of partially dried jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from a Nelder type plantation Canadian Journal of Forest Research 47(7): 910-915
J Cool, RE Hernández (2016). Impact of three alternative surfacing processes on weathering performance of an exterior water-based coating Wood and Fiber Science 48(1): 43-53
I Porth, GQ Bull, J Cool, N Gelinas, VC Griess (2016). An economic assessment of genomics research and development initiative projects in forestry CAB Reviews 11(016): 1-10
VC Griess, R Panwar, J Cool (2015). The potential of mixing timber assets to financially offset negative effects of deer browsing on western redcedar The Forestry Chronicle, 91(4): 436-443
S Kuljich, J Cool, RE Hernández (2013). Evaluation of two surfacing methods on black spruce wood in relation to gluing performance Journal of Wood Science 59(3): 185-194
J Cool, RE Hernández (2012). Effects of peripheral planing on surface characteristiccs and adhesion of a waterborne acrylic coating to black spruce wood Forest Products Journal 62(2): 124-133
J Cool, RE Hernández (2011). Evaluation of four surfacing methods on black spruce wood in relation to poly (vinyl acetate) gluing performance Wood and Fiber Science 43(2): 194-205
J Cool, RE Hernández (2011). Improving the sanding process of black spruce wood for surface quality and water-based coating adhesion Forest Products Journal 61(5): 372-380
J Cool, RE Hernández (2011). Performance of three alternative surfacing processes on black spruce wood and their effects on water-based coating adhesion Wood and Fiber Science 43(4): 365-378
LF de Moura, J Cool, RE Hernández (2010). Anatomical evaluation of wood surfaces produced by oblique cutting and face milling IAWA Journal 31(1): 77-88
RE Hernández, J Cool (2008). Effects of cutting parameters on surface quality of paper birch wood machined across the grain with two planing techniques Holz als Roh- und Werkstoff 66(2): 147-154
RE Hernández, J Cool (2008). Evaluation of three surfacing methods on paper birch wood in relation to water- and solvent-borne coating performance Wood and Fiber Science 40(3):459-469