The Mechanical Dissection Laboratory: Educating Engineers for Industry
Bibliographic record
Abstract
In 1997-98 a major study was done to learn the expectations of Canadian industry for skills in new engineering graduates.It was found that while knowledge of mathematics and basic science was adequate, new graduates' knowledge of design, manufacturing and practical shop-floor knowhow was not.This study and similar US work by the ASME touched off several initiatives to address the deficiencies that were voiced by industry.This research was used in founding the University of Windsor's undergraduate automotive engineering program in 1999 as Canada's first undergraduate level automotive education.The program was designed from the outset to incorporate a unique laboratory experience in the third year Automotive Engineering Fundamentals course.The goal was to provide an experience that would promote comprehension and retention through hands-on exercises that were practical to the field and valued by industry.In the Mechanical Dissection Laboratory (MDL), students are assigned a single cylinder four cycle engine with which they work for the semester.Students run their engine and perform basic performance measurements before and after they dissect, examine and reassemble the unit.They measure key dimensional and operating metrics and investigate the materials and manufacturing methods, tolerances and stack-ups and issues such as assembly methods, tool access etc. Students have access to the manufacturer's drawings during their work and use their own tool kits as well as materials provided by the course instructional staff.The development and operation of the MDL is the focus of this paper.The authors describe the origin of the Lab, its inspiration from a similar facility at the University of Karlsruhe, the philosophy and rationale of the Windsor MDL, design and equipment in the Lab and some reflective comments looking back over the ten years since its inception in the program.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".