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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 OpenAlexaff
Abdolreza Bayesteh, Farid Ahmad, Martin Byung‐Guk Jun

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.

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)0.005
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: Software · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.010

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

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
GenreSoftware

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

Citations0
Published2013
Admission routes1
Has abstractyes

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