A model for measuring products assembly complexity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Complexity is generally believed to be one of the main causes of the present difficulties in manufacturing systems. In this article, product assembly complexity is defined as the degree to which the individual parts/subassemblies contain physical attributes that cause difficulties during the handling and insertion processes in manual or automatic assembly. A product complexity model has been developed by incorporating the information amount and content, as well as the Design For Assembly (DFA) principles for assembled products into an earlier model that was designed for measuring complexity of machined parts. The new model is used to assess the assembly complexity of individual parts using an index for measuring the complexity. Individual indices for parts are aggregated to obtain an overall measure for total product assembly complexity. The new measure accounts for the different parts' assembly attributes as well as their number and variety. An automotive piston and a family of three-pin electric power plugs were used to demonstrate the proposed approach for automatic and manual assembly, respectively. The impact of assembly attributes on product assembly complexity was also tracked. The proposed metric is a useful decision support tool for designers to reduce potential product assembly complexity and associated cost. Keywords: productsassemblycomplexitymetrics
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.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it