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Record W2025562530 · doi:10.1243/095440503321148858

A review of conventional and modern single-point sheet metal forming methods

2003· review· en· W2025562530 on OpenAlexaff
E Hagan, J. Jeswiet

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2003
Typereview
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsForming processesSpinningSheet metalMetal formingMechanical engineeringShear (geology)Materials scienceRoll formingDeformation (meteorology)Material flowIncremental sheet formingSingle pointPoint (geometry)Computer scienceEngineering drawingEngineeringComputer simulationComposite materialGeometrySimulation

Abstract

fetched live from OpenAlex

The use of computers in manufacturing has enabled the development of several new metal forming processes that are based upon older technologies. In this case modifications have been made to traditional forming methods such as conventional spinning and shear forming. These two processes are similar in that the deformation mechanism is localized to a small region under the forming tool. Recent advances in computing power have enabled this localized deformation to be accurately controlled and studied. Spinning, shear forming and flow forming are limited to forming parts that are symmetrical about the revolving lathe axis. Current research has been focused on forming non-symmetrical parts using computer numerical control (CNC) technology, without the need for costly dies. A comparison of traditional and modern forming methods is presented here in an attempt to illustrate the evolution of different incremental sheet metal forming techniques. Emphasis is placed on conventional spinning, shear forming and modern computer-controlled forming methods that are currently being studied.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.033
GPT teacher head0.297
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations154
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering ManufactureSame topicMetal Forming Simulation TechniquesFrench-language works237,207