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Record W2087816063 · doi:10.1115/imece2006-13949

Control of the Clad Height in Laser Powder Deposition Process Using a PID Controller

2006· article· en· W2087816063 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPID controllerControl theory (sociology)Nonlinear systemProcess controlController (irrigation)Process (computing)Laser power scalingLaserPower (physics)Materials scienceControl engineeringComputer scienceEngineeringTemperature controlOpticsControl (management)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Laser Powder Deposition (LPD) process is an advanced material processing technique which has many applications. Despite this fact, reliable and accurate control schemes have not yet fully developed for the process. In this paper, the problem of controlling the clad height in the LPD process is studied. Due to a faster response of the process to change in scanning velocity over the laser power, the scanning velocity is selected as the input control variable. Since the governing equations of the LPD process are complex for designing a controller, an identified nonlinear dynamic model is used. The model is a Hammerstein model with a linear dynamic and a nonlinear memoryless block. The model parameters are identified offline using experimental data. The controller has a proportional-integral-derivation (PID) architecture. The controller was implemented on the real plant to asses its performance in the fabrication of several metallic parts composed of stainless steel.

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 score0.659
Threshold uncertainty score0.336

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 head0.003
GPT teacher head0.190
Teacher spread0.187 · 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

Quick stats

Citations6
Published2006
Admission routes1
Has abstractyes

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