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Record W4406702590 · doi:10.3390/electronics14030427

Design and Performance Analysis of a Novel Group of Translational Parallel Robots for a Three-Axis Grinding Machine

2025· article· en· W4406702590 on OpenAlexaff
Qi Zou, Yuancheng Shi, Shuo Zhang, Haiqiang Zhang, Lijian Li, Guanyu Huang, Dan Zhang

Bibliographic record

VenueElectronics · 2025
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsYork University
FundersNational Natural Science Foundation of ChinaColumbus State University
KeywordsGrindingRobotGroup (periodic table)Computer scienceEngineeringMechanical engineeringEngineering drawingArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

There are limited parallel robots applicable to three-axis grinding machines due to the restricted reachable workspace originating from multiple kinematic chains with spatial kinematic joints. The parallel robot will gain significant potential in the industry if a larger workspace can be achieved. This research introduces a special relationship between upper triangular matrix and parallel robot structures for the purpose of designing a group of novel parallel robots without spatial kinematic joints. The detailed inverse kinematic solution of the selected parallel manipulator is derived in accordance with its straightforward architecture. Several singularity configurations are found on the basis of the first-order kinematic relation. The translational reachable workspace is close to a triangular prism. The dexterity and stiffness performances based on the Jacobian matrix are explored for the chosen parallel manipulator. Both indices display a downward trend as the mobile platform rises higher.

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)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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