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Record W3094395907 · doi:10.37775/eis.2020.2.4

On the sliding mode control for precision machining

2020· article· en· W3094395907 on OpenAlexaff
Luis Rubio Rodriguez, Asier Ibeas, László E. Kollar

Bibliographic record

VenueMérnöki és Informatikai Megoldások · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsSavaria (Canada)
FundersEusko Jaurlaritza
KeywordsMachiningControl theory (sociology)Robustness (evolution)ActuatorMiniaturizationControl engineeringSliding mode controlComputer scienceMode (computer interface)EngineeringMechanical engineeringControl (management)Nonlinear systemPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Precision machining uses linear motor actuators in order to deal with robustness and stability in the broad range of cutting conditions. Those systems are demanding more sophisticated control algorithms in order to fulfil each time more exigent tolerances in the tendency to miniaturization of products and to face more robust dynamics. Sliding mode control family is natural approach to cope with mass and damping variation and external disturbances which are inherent characteristics of cutting processes. This paper compares conventional and super-twisting sliding mode controls on keeping the closed loop dynamics within requirements and reject disturbances created by the cutting forces.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.632

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 communication0.0000.001
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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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