Automated BIM-based CNC file generator for wood panel framing machines in construction manufacturing
Bibliographic record
Abstract
Construction manufacturing companies are endeavoring to integrate new technologies and machinery automation throughout the various project phases in the ongoing shift away from traditional stick-built methods. The current practice in constructing manufacturing supports the integration of automated computer numerical control machines, which can undertake various operations and thereby reduce manual work. However, shop drawings need to be obtained from the building information model and imported to third-party software (such as computer-aided design / computer-aided manufacturing software) to generate the corresponding computer numerical control codes. This underscores the need for a fully automated solution for computer numerical control machines in construction manufacturing that reduces the reliance on third-party software, thereby reducing the time, effort, and cost otherwise spent on managing multiple software solutions. As such, the aim of this research is to develop a building information modelling-based automated tool to serve as a direct connection between the building information modelling environment and the automated machine. The tool facilitates the generation of a computer numerical control file directly from the building information model that will serve as an input to an automated wood-wall framing machine. For the wood framing machine under study, a set of rules was developed by which to generate the computer numerical control file directly from the building information model. This included developing an identification system for the main operations that can be performed by the machine and extracting information from the model that may be of relevance to the process. An add-on was then developed in Autodesk Revit to generate the computer numerical control file. The proposed methodology was validated by generating computer numerical control files using the developed add-on and inputting them to the machine. Using the generated computer numerical control files, the machine was found to be capable of properly performing the operations as planned.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".