Complexity mapping of the product and assembly system
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
Purpose The purpose of this paper is to present methods for assessing and mapping the complexity of products and their assembly. In cases of complexity of assembly it is important to consider and model at the product design stages when only data about individual parts/products and their assembly attributes are known. Assessing the complexity of assembly systems, based on the attributes of their components, is an essential step towards designing them for the least complexity. Design/methodology/approach This paper presents a mapping method between the complexity of products and their variants and complexity of the system needed to assemble them. A method has also been developed to assess and compare the complexity of assembly systems based on the characteristics of their physical components for comparison and re‐design to reduce complexity. Findings The complexity dependency matrix estimates the average assembly equipment complexity for a certain product based on the interactions between parts handling, insertion and assembly attributes and assembly system functions. An automobile engine piston, domestic appliance drive, car fan motor and a three‐pin electric power plug products were used to demonstrate the application of the developed methodology. Originality/value The developed methods can be used by products and assembly systems designers to identify and alleviate major sources of complexity.
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 | 0.001 | 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 | 0.002 |
| 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 it