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Record W4386213415 · doi:10.30955/gnc2023.00496

Paradigm Shift Towards Integrated Sustainability and High Performance Machining

2023· article· en· W4386213415 on OpenAlexafffund
Helmi Attia

Bibliographic record

VenueGlobal NEST International Conference on Environmental Science & Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsMcGill UniversityNational Research Council Canada
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaPartenariat Canadien Contre Le Cancer
KeywordsSustainabilityProductivityMachiningProcess (computing)Manufacturing engineeringQuality (philosophy)LubricationRisk analysis (engineering)Production (economics)Computer scienceEngineeringEnvironmental economicsBusinessMechanical engineering

Abstract

fetched live from OpenAlex

The environmental impact of machining is presented in terms of the damage to human health, ecosystem quality, and resources. To reconcile the conflicting requirements of economic growth and environmental protection, a paradigm shift towards integrated sustainability and high performance machining is discussed in the light of the new industrial revolution I5.0. Implementation of the sustainability strategy showed that the use of innovative and eco-friendly cooling/ lubrication methods in machining can significantly reduce the environmental impact and improve productivity, part quality, and process economics at the same time. Implementation of the resilience strategy, through a cyber-physical adaptive control system, showed up to 50% and 35% of combined reduction in the production cycle time and cost, respectively, as well as extending the tool life and eliminating the part damage.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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
Published2023
Admission routes2
Has abstractyes

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