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Record W2034458339 · doi:10.1016/j.procir.2014.02.032

Analysis of Friction and Burr Formation in Slot Milling

2014· article· en· W2034458339 on OpenAlexaff
Seyed Ali Niknam, Victor Songméné

Bibliographic record

VenueProcedia CIRP · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMachiningEnhanced Data Rates for GSM EvolutionMechanical engineeringMaterials scienceShear (geology)EngineeringEngineering drawingComposite material

Abstract

fetched live from OpenAlex

Burr formation is one of the most common and undesirable phenomenon occurring in machining operations that reduces assembly and machined part quality, and it should be avoided or at least reduced. To remove burrs, a non-value added secondary operation known as deburring is required for post-processing and edge finishing operations. Among conventional machining operations, milling burr formation is a very complex mechanism. Therefore, research and close attention are still needed in order to minimize and control milling burr formation. This could be achieved by effective burr prevention through adequate understanding of the basic mechanisms of burr formation and an accurate proposal of optimum cutting parameters. In recent reported works in literature, exit up milling side burr was characterized as the longest and thickest milling burr which is formed by loss of material during exit burr formation. Since burr thickness is a critical parameter for better selection of the deburring time and method, a good knowledge on the effects of cutting parameters, friction and tool geometry and coating on this burr is important for better selection of deburring methods. Although friction angle has a direct proportion to negative shear angle, radial and tangential cutting forces, but very limited information is still available on correlative studies between burr size and friction angle in milling operation. This paper presents the effects of cutting parameters on friction angle and the correlation between friction angle and exit up milling side burr thickness during slot milling of aluminum alloys. To that end, a computational algorithm that was recently proposed by authors is used to calculate the friction angle λ for each material when using specific levels of cutting speed, feed per tooth and undeformed chip thickness. Experimental results show that lower friction angle is resulted when using larger chip load. Consequently, larger friction angle is obtained when exit up milling side burr thickness decreases and exit bottom burr thickness increases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations21
Published2014
Admission routes1
Has abstractyes

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