Bibliographic record
Abstract
Ultra-Span manufactures equipment for use in the precast concrete industry. Their goal is to provide their customers with equipment designed with simplicity in mind to increase throughput, reduce downtime, and increase profitability. Ultra-Span set a target to reduce the manufacturing cost of the SMA-400 hollow-core saw by $3000 as they are 60% over their expected budget for the machine. Furthermore, the machine operator cannot easily line the saw blade with the intended cut location due to poor line of sight. Additionally, the red laser guide is too dim in bright ambient lighting. This results in the operator dismounting multiple times to line up the saw blade. The team redesigned the mainframe tubing assembly and lifting beam to reduce material costs. Robot Structural Analysis was used to optimize the tubing thickness of each member in the mainframe tubing assembly by up to 50%. The cost savings of the final design of the mainframe tubing assembly is estimated to be $1170, while satisfying the required minimum safety factor of two. Inventor stress analysis was used to verify that the new lifting beam has a minimum safety factor of five. The final design of the lifting beam features a 7x3x1/4 rectangular steel tube, a 1-inch thick steel hoist plate, and 1-inch thick steel end plates. The cost savings of the final design of the lifting beam is estimated to be $706.34. Design simplifications reduced the cost of the saw by $861.77. These simplifications include reducing the number of toggle switches to one, redesigning the back wall panel, modifying the side fastener layout, removing the hydraulic hose mounts, and making the slab clamps optional. The team proposes to subcontract the mainframe tubing assembly to reduce labor costs. An estimate of the labor required to assemble the tubing assembly was obtained from Standard Machine Works in Winnipeg. The theoretical subcontracting of the assembly would reduce the labor cost of the saw by $2570. For the secondary objective, ten concepts were generated to improve operator visibility in lining up the saw blade with the intended cut. The team selected the best concept based on cost, simplicity, ease of use, reliability, and ease of lining up the cut. Ultimately, the team determined that adding a camera and laser system as a single solution is the best method to line up the saw blade with the intended cut location. The 10mW green laser is five times brighter than the current 10mW red laser, allowing the operator to see the laser in bright ambient lighting. The cost of the camera system is $561.61 and the two green lasers cost $904. Overall, the total cost of Ultra-Span's SMA-400 saw was reduced by $4145.18, which exceeded the target of $3000. Additionally, the operator's ability to line up the saw to the intended cut location is improved. In conclusion, the main objectives of the project are achieved while meeting all the constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".