PROJETO DE UM SENSOR DE TORQUE SEM FIO PARA EIXOS GIRANTES
Bibliographic record
Abstract
Este artigo descreve a implementacao de um sensor de torque sem fio de baixo custo para aplicacoes em eixos giran-tes. O instrumento e capaz de medir o torque em maquinas rotativas, enviando, sem fio, o sinal relacionado a grandeza para um dispositivo Android, que funciona como sistema de aquisicao de dados. O conceito do projeto e baseado em medicoes de tensao utilizando extensometros resistivos (strain gauges). O projeto inclui um conjunto de straing gauges 350 Ω em ponte completa, um kit Arduino UNO, um adaptador SD para Arduino, um modulo Bluetooth e um conversor analogico-digital (ADC) de 24 bits. A implementacao consiste em instalar os strain gauges, programar o Arduino e fabricar o circuito ADC. Apos essas etapas, o sensor e testado em um braco de alavanca. Os resultados mostram que o sistema pode realizar medicoes de torque com uma resolucao de 0,0138 N·m, exibindo, em tempo real, a curva de torque em uma tela de smartphone/tablet. Assim, o sensor de torque apresentado representa um instrumento versatil, preciso e de baixo custo para auxiliar na avaliacao de desempenho e em projetos de maquinas girantes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".