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Record W2799872363 · doi:10.1139/tcsme-2015-0023

SURFACE ROUGHNESS AND CUTTING FORCES IN TURNING OF TOOL STEEL WITH MIXED CERAMIC AND CUBIC BORON NITRIDE CUTTING TOOLS

2015· article· en· W2799872363 on OpenAlexvenueno aff
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Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
FundersSüleyman Demirel Üniversitesi
KeywordsBoron nitrideCeramicMaterials scienceMachiningSurface roughnessMetallurgyCutting toolHardened steelSurface finishTool wearComposite materialMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Tool steel has been widely used, especially to manufacture forming dies and molds by machining processes. Generally, cubic boron nitride (CBN) and ceramic tools are recommended for finish machining a specific steel. This study contributes to filling the research gap for the selection of low- content CBN tools or mixed ceramic tools for turning of hard tool steel. The turning tests were conducted to determine the performance of CBN and the mixed ceramic tools in turning soft (HRC22) and hard (HRC52) H13 tool steel with different cutting speeds, feed rates and depths of cut. ANOVA was used to determine the interaction of the cutting parameters on the surface roughness and cutting forces obtained from turning tests. The results indicate that the surface roughness in hard turning was lower with the CBN tool than with the ceramic tool. On the other hand, the cutting forces in turning with the ceramic tool were lower. Acceptable regular chip formation increases with the cutting speed for each tool.

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: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.461

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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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