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Record W2597052691 · doi:10.1109/tase.2017.2667709

Adaptive Neural-Network-Based Active Control of Regenerative Chatter in Micromilling

2017· article· en· W2597052691 on OpenAlex

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

VenueIEEE Transactions on Automation Science and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsControl theory (sociology)ActuatorCompensation (psychology)Controller (irrigation)Artificial neural networkControl engineeringEngineeringAdaptive controlProcess (computing)VibrationControl systemComputer scienceActive vibration controlVibration controlControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, an active control approach using two piezoelectric actuators (PZTAs) and an adaptive controller is investigated for suppressing the two-DOF regenerative chatter in micromilling. The PZTAs are utilized as active control elements to provide force compensation for chatter suppression. First, the dynamical model of micromilling process is demonstrated. Then, an adaptive controller is developed by employing neural networks to approximate the unknown dynamics of the cutting system and the unknown bounding functions related to the time-delayed tool vibrations, and applying the Lyapunov-Krasovskii functional to aid in treating the time-delayed effect of the regenerative mechanism of chatter. By employing the developed control approach, the tool vibrations in two directions vertical to each other are successfully suppressed. Finally, simulations are presented to validate the effectiveness of the developed control approach.

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)0.000
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 score0.869
Threshold uncertainty score0.427

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 head0.012
GPT teacher head0.232
Teacher spread0.221 · 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