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Record W2105487349 · doi:10.1115/imece2013-65423

Computer-Aided Manufacturing (CAM) Software Development for Laser Machining

2013· article· en· W2105487349 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVolume 2A: Advanced Manufacturing · 2013
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMachiningSoftwareComputer scienceTrajectoryLaser beam machiningComputer-aided manufacturingLaserSurface micromachiningEngineering drawingLaser ablationMachine toolComputer Aided DesignMechanical engineeringEngineeringLaser beamsOperating system

Abstract

fetched live from OpenAlex

A novel computer-aided manufacturing (CAM) software system is proposed for laser ablation machining process. The algorithms and prototype software system is designed to offer efficient optimization of tool path for controlled delivery of laser energy into work-piece. The software simplifies part program creation and maintains constant velocity of the sample stage for each segment of a complex tool trajectory. These features enable efficient deposition of laser energy into the work piece and therefore, reduction in heat-affected zone is expected in laser ablation based micromachining. The reported software provides fast modification of tool path, automatic and efficient sequencing of path elements in a complicated tool trajectory, location of reference point and automatic fixing of geometrical errors in imported drawing exchange files (DXF) or DWG format files.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.207
Teacher spread0.199 · 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