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Record W2484139512 · doi:10.3390/e18080290

Control of Self-Organized Criticality through Adaptive Behavior of Nano-Structured Thin Film Coatings

2016· article· en· W2484139512 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

VenueEntropy · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceTribologyPhysical vapor depositionCoatingMachiningNano-Scanning electron microscopeThin filmNanoscopic scaleEnhanced Data Rates for GSM EvolutionComposite materialTool wearForensic engineeringNanotechnologyMetallurgyComputer science

Abstract

fetched live from OpenAlex

In this paper, we will develop a strategy for controlling the self-organized critical process using the example of extreme tribological conditions caused by intensive build-up edge (BUE) formation that take place during machining of hard-to-cut austentic superduplex stainless steel SDSS UNS32750. From a tribological viewpoint, machining of this material involves intensive seizure and build-up edge formation at the tool/chip interface, which can result in catastrophic tool failure. Built-up edge is considered to be a very damaging process in the system. The periodical breakage of the build-ups may eventually result in tool tip breakage and, thereby, lead to a catastrophe (complete loss of workability) in the system. The dynamic process of build-up edge formation is similar to an avalanche. It is governed by stick-slip phenomenon during friction and associated with the self-organized critical process. Investigation of wear patterns on the frictional surfaces of cutting tools using Scanning Electron Microscope (SEM), combined with chip undersurface characterization and frictional (cutting) force analyses, confirms this hypothesis. The control of self-organized criticality is accomplished through application of a nano-multilayer TiAl60CrSiYN/TiAlCrN thin film Physical Vapor Deposition (PVD) coating containing elevated aluminum content on a cemented carbide tool. The suggested coating enhanced the formation of protective nano-scale tribo-films on the friction surface under operation. Moreover, machining process optimization contributed to further enhancement of this beneficial process, as evidenced by X-ray Photoelectron Spectroscopy (XPS) studies of tribo-films. This resulted in a reduction of the scale of the build ups leading to overall wear performance improvement. A new thermodynamic analysis is proposed concerning entropy production during friction in machining with buildup edge formation. This model is able to predict various phenomena and shows a good agreement with experimental results. In the presented research we demonstrated a novel experimental approach for controlling self-organized criticality using an example of the machining with buildup edge formation, which is similar to avalanches. This was done through enhanced adaptive performance of the surface engineered tribo-system, in the aim of reducing the scale and frequency of the avalanches.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.264
Teacher spread0.257 · 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