Design and automatic assembly sequence generation of a d.c. motor
Bibliographic record
Abstract
A design–for–assembly (DFA) method is used to analyse a family of d.c. motors and to design them with emphasis on meeting the criteria of the market demands while allowing for robotic assembly. Production cost, production facilities, development time and tooling were also taken into consideration. This article describes both old and new designs and highlights those design changes which have resulted in a 68 per cent reduction in parts and a 60 per cent reduction in assembly time, according to the DFA analysis. A market survey was performed to identify the applications in the automotive market for such motors. It identified the possible model variations such as shaft length, speed/torque specifications, double–ended shaft possibilities and mounting–bracket positions. The variation in motor models, low production batch sizes and fluctuating market demands make flexible and programmable assembly a very attractive option. The redesigned motor is currently being assembled manually in a production environment, and technical and economic proposals have been completed for automating the final motor assembly process. A knowledge–based approach has been used to generate the assembly sequence of the redesigned motor automatically. A new failure–based description language was used to express the motor design features and their functional relationships. Expert assembly rules were then used to generate the motor assembly sequence automatically.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".