Bibliographic record
Abstract
<div class="htmlview paragraph">Automotive industry is one of the major drivers of the global economic growth and a leader in the research and development of advanced manufacturing technologies. Machining and automation are primary processes for product development and production for automotive industry because of their high efficiency and flexibility. Today's automotive products are complex, sophisticated and have a large variation for different models. The demand for these products is mostly in small to medium lot sizes but with a large variation. Although computerized numerical control (CNC), computer-aided design (CAD) and computer-aided manufacturing (CAM) are well developed today but still fall short in efficiency to produce these products due to a variety of designs and customizations, short lead time and low volume production. In order to address some of the deficiencies, Integrated Manufacturing Technologies Institute (IMTI) of the National Research Council of Canada (NRC), is working on a number of advanced manufacturing processes. This paper focuses on the new and innovative ideas being developed at IMTI to enhance the performance and flexibility for the manufacturing of automotive products, from the development of parts, moulds, tools and dies to material handling, production and assembly. In particular, an integrated scheme for the development of a series of machining and automated systems that include automatic CNC programming, online inspection, reference free setup, error detection and compensation, and intelligent monitoring and control will be described.</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".