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1A1-D08 Study on Metal Mold Polishing Robot : Experiment on Flat Surface Polishing under Constant Polishing Pressure Condition(Robots for Works)

2011· article· en· W2627598770 on OpenAlex
Shunsuke ISSHIKI, Koji SHIBUYA, Yukihide MATSUNO, Nobuya MARUYAMA, Tadayoshi YAMADA, Suguru MATSUSHITA, Hiroshige KAWACHI

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

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2011
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsXebec (Canada)
Fundersnot available
KeywordsPolishingSurface roughnessRobotMoldSurface finishMaterials scienceConstant (computer programming)Mechanical engineeringSurface (topology)Engineering drawingComputer scienceComposite materialEngineeringMathematicsGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

The final goal of this study is to develop a robotic system that can precisely polish metal molds instead of skilled persons. We have been trying to build such a system using an industrial robot with six joints, and soft and ceramic whetstones to polish metal molds. In this paper, we build a machine that can polish under constant polishing pressure condition. Then, we conducted an experiment to uncover relationships between the number of polishing and surface roughness. As a result, we found out that surface roughness was almost same when the number of polishing varied. However, the standard deviation of the surface roughness varies according to the number of polishing. Also we attached a six axes force sensor to an industrial robot to measure forces during polishing.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.049
GPT teacher head0.271
Teacher spread0.222 · 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