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Record W2008111124 · doi:10.1260/1478077054214460

3-Axis CNC Milling in Architectural Design

2005· article· en· W2008111124 on OpenAlexaff
Robert Aitcheson, Jonathan Friedman, Thomas M. Seebohm

Bibliographic record

VenueInternational Journal of Architectural Computing · 2005
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsProcess (computing)Scale (ratio)Rapid prototypingEngineeringEngineering drawingFocus (optics)Manufacturing engineeringPresentation (obstetrics)Computer scienceMechanical engineeringSystems engineeringProgramming language

Abstract

fetched live from OpenAlex

Physical scale models still have a role in architectural design. 3-axis CNC milling provides one way of making scale models both for study purposes and for presentation in durable materials such as wood. We present some types of scale models, the methods for creating them and the place in the design process that scale models occupy. We provide an overview of CNC milling procedures and issues and we describe the process of how one can creatively develop appropriate methods for milling different types of scale models and materials. Two case studies are presented with which we hope to convey not only the range of possible models that can be machined but also the way one creatively explores to arrive at appropriate milling strategies. Where apposite, we compare 3-axis CNC milling to newer technologies used for rapid prototyping but rapid prototyping is not a primary focus.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.012
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2005
Admission routes1
Has abstractyes

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