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

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