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Record W2991550230 · doi:10.1115/msec2019-3026

Experimental and Simulation Study of Single Point Incremental Forming of Polycarbonate

2019· article· en· W2991550230 on OpenAlexaff
Saurabh Rai, Rakesh Kumar, Harish Kumar Nirala, Kevin Francis, Anupam Agrawal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsLakeridge Health
Fundersnot available
KeywordsPolycarbonateMaterials scienceComposite materialUltimate tensile strengthWork (physics)Forming processesProcess (computing)Fracture (geology)Point (geometry)Mechanical engineeringStructural engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Single point incremental forming (SPIF) is more accurate and economical than the conventional forming process for customized products. Majority of the work in SPIF has been carried out on metals. However, polymers are also required to shape. Polycarbonate has wide application in safety glass, bottles, automotive and aircraft industry due to its transparent as well as attractive processing and mechanical properties as compared to other polymeric plastics. In present work, the Polycarbonate (PC) sheet of thickness 1.8 mm is deformed to make a square cup at different angles. Tensile testing is done to analyze the effect of wall angle on the deformed cup. This work illustrates the effect of the SPIF process on material strength in a different directions (vertical and horizontal) of the final deformed product. Tool forces are evaluated using ABAQUS® simulation for SPIF. Numerical simulation approach is used to calculate the fracture energy, which utilizes the force-displacement curve of the specimen and is verified.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.265
Teacher spread0.249 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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