Ontology-Based Knowledge Modeling for Frame Assemblies Manufacturing
Bibliographic record
Abstract
Ontology-Based Knowledge Modeling for Frame Assemblies Manufacturing Shi An, Pablo Martinez, Rafiq Ahmad and Mohamed Al-Hussein Pages 709-715 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: As modular construction becomes popular, an increasing number of products are prefabricated in an offsite construction environment. While improving the productivity and efficiency of construction-oriented production, it also raises the complexity of process planning. Although the specifications of a product are fully defined by Building Information Models (BIM), no information is provided on how construction products are manufactured and assembled. This paper proposes an ontology-based approach aimed to link construction-oriented product assemblies and manufacturing resources using manufacturing operations. By identifying intersections of connecting members of a product assembly, feasible manufacturing methods and resources are determined based on expert knowledge and machine configurations. The proposed approach is validated using a wood frame assembly. Keywords: Building information modeling; Ontology modeling; Offsite construction; Construction automation; Construction manufacturing DOI: https://doi.org/10.22260/ISARC2019/0095 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".