Engineering Drawing Information Extraction for the Estimation of Carbon Footprint
Bibliographic record
Abstract
Abstract Manufacturers often provide engineering drawings and cost estimates in engineering proposals, but rarely include sustainability metrics such as carbon footprint. These metrics are becoming crucial as businesses aim to reduce their carbon footprint to address climate change. Although these metrics can be manually estimated from the Bill of Materials (BoM) and drawing annotations for each part, the process is inefficient and prone to errors. An automated pipeline to extract and analyze sustainability-related data directly from engineering drawings is proposed in this work. The approach integrates a rule-based table structure recognition model with Optical Character Recognition (OCR) to extract structured information from engineering drawings. It is compared with state of the art model UniTable, and both their performances were evaluated on an engineering drawing in the mining industry, using Tree-Edit-Similarity score (TEDs) and structural-TED score (S-TEDs). The extracted data is then analyzed to identify material properties and estimate the power consumption and carbon footprint of the manufactured part. An overview of the automation pipeline and a comparative analysis of extraction techniques will be presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".