Radius Putty Machine Tool Redesign
Bibliographic record
Abstract
This report presents the approach taken to design a solution to a Radius Putty Application System under the University of Manitoba Innovative Design for Engineering Applications (UMIDEA) Program for Carfair Composites Inc. (Carfair). The system was created by Magnum Venus Products (MVP) by modifying an existing resin spray gun. The system was redesigned to be used to fill putty in areas of sharp radii. However, the system proved to not meet all of their needs sufficiently, thus, it had been retired. Upon investigation, there were several issues with the putty gun. The primary issues were an inconsistent catalyst concentration in the putty as well as over-spray occurring, where putty was sprayed into areas beyond where it was intended. After investigating, the team developed a list of concepts as to how to rectify the issues by utilizing various concept generation techniques. From there, six primary concepts were put into a Weighted Decision Matrix. This provided the top three ranked solutions, per Carfair’s request. The UMIDEA team chose to begin by investigating the feasibility of the top-ranked solution for catalyst concentration inconsistency issue. The solution was to remove the putty supply pump and obtain a putty with a lower viscosity. The putty supply pump had been added to the putty system by Carfair. They assumed this pump was required due to the higher viscosity putty. However, when the team tested the system, it was demonstrated that the main pump was able to pump sufficiently. This led the team to believe that perhaps a second issue with the system was material storage. Upon inspecting the previous putty storage practices, it was observed that the containers would sit open to the air for extended periods of time. This caused the putty to begin to harden, and it a possible cause for why the main pump had previously been unable to pump the putty. The investigation also demonstrated that in varying the pressure of the system and/or the catalyst ratio, the consistency in the catalyst concentration varied greatly. Unfortunately, there were putty supplier issues, and Carfair was only able to obtain the putty very late into the project timeline. The testing did not provide a sufficient result, and there were multiple possible sources of error. Due to time constraints, the testing of the final design was far more brief than the UMIDEA team had planned. The testing found that the optimum settings were 35 psi pressure with a 2.5 percent catalyst concentration. However, the team prepared a list of further testing that should be performed in order to further investigate these settings, as the result was not within the Client’s specifications.Two different components were also designed. To eliminate the over-spray issue, the air hose connections in the putty gun were removed. A new conical nozzle tip was designed in order to increase the ease of extruding the bead into the sharp radii. A foot for the putty infeed transfer rod was designed to be used in place of where the supply pump had previously connected. The transfer rod would often sit flat on the base of the putty bucket, and would suction to the bottom, resulting in poor putty flow. The foot was designed with the intent of adding gaps beneath the rod. The team also made new work instructions on how to properly use the retired putty application system. A list of recommendations for future investigation was also prepared. The team was able to achieve a 69 percent reduction in the project cost from the approved budget and predicts that by following the list of future recommendations, all other project needs and specifications would be met.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".