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Record W4200509153 · doi:10.32920/16777663

Development Of A Biaxial Testing Apparatus

2021· preprint· en· W4200509153 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicIoT-based Control Systems
Canadian institutionsMcMaster UniversityToronto Metropolitan University
Fundersnot available
KeywordsArduinoStepperMicrocontrollerSoftwareComputer scienceCompression (physics)Mechanical engineeringEngineeringEmbedded systemMaterials scienceOperating systemComposite material

Abstract

fetched live from OpenAlex

Material testing is a crucial part of engineering design and development, especially in aerospace engineering as it is more cost effective to test an element or component than doing a full-scale test on the completed part. Typically, uniaxial testing is carried out to characterize a material, which is adequate for finding the properties of the material. However, these kinds of tests are inadequate for simulating the real-world loading that a component may experience in its life cycle. Therefore, this project’s goal was to develop a low-cost biaxial testing apparatus using off-the-shelf components, including the Arduino Uno microcontroller (“Arduino”), stepper motors (“motors”), and load cell. This report outlines the development of the software required to operate the motors and read output value of the load cell. The Arduino code used to control the motors was developed using open-source code available on GitHub and the Stepper library, which contains the required functions for controlling the motors. The Arduino code can be used to determine the strain rate of up to 11 𝑚𝑚/𝑚𝑖𝑛, as well as the type of loading (tension or compression) along each axis.<br>

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.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.826
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.002
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 head0.049
GPT teacher head0.253
Teacher spread0.205 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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