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Record W4392719211 · doi:10.23977/jemm.2024.090102

Research on Space Cam Automation Design and Manufacturing Scheme Based on CAD and CAM Systems

2024· article· en· W4392719211 on OpenAlexvenueno aff
Yingyu Tao, Sihui Tao

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2024
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsnot available
Fundersnot available
KeywordsCADScheme (mathematics)AutomationComputer-aided manufacturingManufacturing engineeringSpace (punctuation)Electronic design automationComputer scienceEngineeringEngineering drawingEmbedded systemMechanical engineeringMathematicsOperating system

Abstract

fetched live from OpenAlex

The aim of this study is to realize the fast and accurate design and manufacture of spatial CAM by introducing CAD/CAM system. By using Pro/ENGINEER software widely used in CAD/CAM system, the follower displacement curve is quickly and accurately drawn according to the curve equation of the motion specification of the spatial CAM follower, and the 3D model of the spatial CAM is created according to the curve by Pro/Feature module. Then, the contour surface quality of the spatial CAM is checked by surface analysis, and the detection results are fed back to the modeling design of the spatial CAM. After analyzing the machining theory of the spatial CAM, according to the theory and the modeling completed in the Pro/Manufacture module, the NC machining code of the spatial CAM is automatically programmed. Based on the feedback data of the surface inspection information, the design and manufacturing process of the spatial CAM are continuously optimized. Therefore, the created contour surface has high precision, excellent kinematic and dynamic characteristics. This research provides an effective method and scheme for the automatic design and manufacture of space CAM.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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