Design of an Automated Scrap Sheet Removal System
Bibliographic record
Abstract
Ryerson Canada, is one of the largest metal distributors in North America. The team has partnered with Ryerson to design an Automated Scrap Sheet Removal System to improve the company’s cut-to-length process. The current offload process takes 20 minutes per coil and results in a lost production time of 330 man-hours per year, equating to a total loss of $117,000 USD annually. Through a series of site visits and client meetings, the team was able to develop a list of client needs and specifications to solidify the objective of the design. From the client needs, the team determined two separate processes within the removal operation as a basis to separate the system into two design components. Component 1 was designed to offload the scrap sheet metal from the cut line, whereas Component 2 was designed as a motorized cart system to receive the scrap metal sheets from Component 1 and move it to the crane area. The design of Component 1 incorporates staggered roller wheels to assist in sheet metal sliding, gussets for additional side-loading stability, and compact packaging when folded for the design to fit in the designated space. The hydraulic cylinders specified for this setup have a stroke length of 8” and will actuate the lift to full extension in 5 seconds. At full extension, a variable height stopper moves below the lift table surface and allows the sheet to slide down the lift. Analytical and numerical analysis such as yield, buckling, and fatigue were used to optimize the scissor design of Component, and any other failure modes were investigated through a Failure Modes and Effects Analysis (FMEA). The design of Component 2 consisted of reinforcing two existing sheet metal skids for conversion to motorized carts. The upper cart uses two HSS steels placed directly below the existing skid structural tubes, whereas the lower cart incorporates one section of HSS steels centered on the existing skid. These carts roll on a wide flange beam setup for use as a rail system. Both carts incorporate single drive wheels driven by reduction gearboxes and 3-phase electric motors. Power transmission is accomplished by a No. 40 chain drive between the gearbox output shaft and drive wheel on each cart. Analytical and numerical analysis were also used to optimize the design of Component 2, and any other failure modes were investigated through a Failure Modes and Effects Analysis (FMEA). The integration of the two design components led to an overall design that meets the client needs by safely withstanding possible modes of failure and decreasing the cycle time by 76%. Between Components 1 and 2, the total cost is approximately $12,000 CAD in materials, which is under the allotted budget of $15,000 CAD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".