Integrated products–systems design environment using Bayesian networks
Bibliographic record
Abstract
Providing new design environments to assist manufacturing engineers is essential to decrease products’ lead time. Complex systems will be better designed and utilised to manufacture more products efficiently if systems–products’ relationships are retrieved automatically and effectively. In this paper, a design environment using a Bayesian network is proposed. It inducts the relationships within the products’ and machine’s domains and maps the relationships between the two domains. The design environment incorporates Necessary Path Condition used in structure learning in a Bayesian network, estimation–maximisation (EM), k-most probable configurations algorithm, and d-separation concepts to understand and analyse these relationships, hence, it facilitates synthesising new systems and product. Two case studies are presented involving: (1) milling machines and their corresponding machined parts, and (2) tools’ inserts used in grinding and their corresponding fixtures. In addition, a theorem is proposed, proved and discussed to justify the use of the Bayesian network for detecting the products–machines relationships. Results show that, unlike other design methodologies, the Bayesian networks can provide adaptable design environment by analysing the interactions between existing manufacturing entities such as machines/products and fixtures/inserts’ specifications in a reverse engineering manner, without clearly identifying all the relationships between them a priori. The Bayesian network’s inference capabilities are used to determine the most suitable machines/fixtures for new parts, and deduce the composite part/product that can be manufactured using newly acquired machines.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".