Down Feather Extraction Project Design
Bibliographic record
Abstract
Canada Goose requires a redesign of their down feather extraction process to improve their sustainability efforts. The objective of the project is to improve the process increasing the efficiency of down extraction. The extracted down will be processed and re-used in future products by the company. The design should improve the current extraction rate of approximately 50% to upwards of 90% for all Canada Goose jackets while minimizing human intervention in the process. Additional needs identified by the client include a maximum budget of $150,000 CAD, maintained or reduced cycle time, durability, ease of cleaning, and an appropriate noise level during operation along with worker safety. This report contains information on the project scope and objectives with details provided by the client. This is followed by the concept generation and selection process applied to select the final design. Furthermore, this report presents the design developed to improve the extraction of down feathers, accompanied by a Computer-Aided Design model, engineering drawings, Bill of Materials, and a Standard Operating Procedure. Recommendations and future work items to improve the process are included as well. The final design extraction rate is greater than 90% through testing in the prototype developed. Different jacket loads were tested to determine the point at which the extraction rate was below the required 90%, after which the size of the design was scaled accordingly. The cost for the design including materials and assembly is $7480 CAD with a contingency reserve of 15% added to material costs. The cycle time has been reduced to 15 minutes for lightweight jackets and 25 minutes for regular jackets from the 45 minutes currently. The final design uses airflow to create separation between the jacket pieces and down feathers in a 24” diameter duct through turbulence. The design ductwork requires an expansion from a 6” spark-resistant blower output to the 24” separation duct while maintaining uniform airflow. The design incorporates an expansion to 16” before expanding to 24” for a smaller change in static pressure. The 24” separation duct is followed by a reduction to the 16” diameter continuing to a collection system of the client’s choosing. An air filter present at a T-junction prior to the collection system leads to a muffler which reduces noise. The jackets are cut using scissors and inserted through an upward duct at a wye connection sealed by a hand-removable end cap. The design incorporates a variable frequency drive to control airflow within the ductwork. A mesh screen in the separation duct stops the jacket pieces from following the airflow and down into the collection system. Maintenance and cleaning of the system can be done through hand-removable access doors on the bottom 16” segment and the 24” duct below the mesh screen. Cleaning of the ducts is required between cycles to reduce the risk of system blockage. After the extraction process the jacket pieces are removed Final Report iii December 9, 2020 through the access door on the 16” duct at the bottom. The system is supported by mounting rails and support straps to the floor and walls and attaching hangers to the ceiling. As identified above, all the client needs have been addressed and were an integral component of the concept generation and selection process leading to the final design. The goals for this project have been achieved by implementing a design that is composed of HVAC ducting and dependent on the airflow within the ducting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".