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Record W4249278153 · doi:10.32920/ryerson.14651958

Novel Cylindrical/Spherical Scanner Configuration for Reverse Engineering

2021· preprint· en· W4249278153 on OpenAlexaff
Medhat Samaan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLaser scanningScannerObject (grammar)Rotation (mathematics)Computer visionComputer scienceTable (database)Artificial intelligenceLaserReverse engineeringStructured lightPath (computing)Computer graphics (images)OpticsPhysics

Abstract

fetched live from OpenAlex

Laser scanners are often used in geometric reverse engineering to generate a CAD drawing out of an existing physical part. This often requires careful path planning to ensure the correct stand-off distance of the sensor, to prevent any collisions of the sensor with the target object and to prevent any scan occlusions in the resulting data. A dedicated system was constructed to collect data using a 3-axis cylindrical/spherical device. The benefit of this scanning system is the ability to scan completely around the target object, including the bottom of the object. A stripe-type laser diode is used along with a CCD camera. The target object sits on a transparent plexiglass table, which can be rotated 360⁰. Both the laser and the CCD are mounted on an arm, such that they can be traversed along a linear path, as well as being rotated 360⁰ around the table. This permits the device to scan in either spherical or cylindrical paths with 360⁰ rotation around the target object. This system allows for the quick scanning of any object while minimizing the number of occlusions in the resulting scan data.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.217
Teacher spread0.201 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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