Machining Versus Molding Tolerances in Manufacturing Automotive Sealing Systems
Bibliographic record
Abstract
The automotive industry has been at the forefront of converting traditional metal parts to plastics. The latter surely offer greater design freedom, opportunity for consolidation, fewer assembly operations, reduced secondary finishing, weight reduction, lower total system costs, a range of properties tailored to specific applications, the ability to withstand temperatures, immunity to most chemicals and corrosive environments. They offer processing in many colors, electrical non-conductivity (insulation from electrical shocks), good thermal breaks (“warmth-to-the-touch”), and low sound transmission (tendency to muffle noise). Nonetheless, plastics have only tapped an estimated 15% of their tremendous potential to replace metals. This is particularly to increase with newer high-performance plastics, increasing sophistication in alloying and blending technologies, and use of computer-aided design and engineering (CAD/CAE) systems. The latter enable engineers to visualize complex parts and molding tools more effectively and faster than ever before. This article identifies fundamental steps and requirements to conduct an efficient and successful conversion of metallic parts to plastics, reviewing the replacement design process from concept to production; an under-the-hood rear retainer for Ford Motor Company is detailed as a case study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".