A Multiaxis Force Sensor Based on Pre-Strained Piezoresistive Film Strips
Bibliographic record
Abstract
Soft multi-axis force sensors are essential in automation and robotic manipulations for dexterous applications. Carbon-based composites are quality candidates for sensing elements of soft multi-axis force sensors due to its piezoresistivity originated from electron tunneling effect. However, low cost and fast-prototyped soft multi-axis force sensors based on tunneling effect of carbon-based composites are still in their infant. This work investigated a soft multi-axis forces sensor utilizing Velostat film strips as the core sensing elements. A unique serpentine structure of Velostat strips has been designed and implemented as piezoresistors for the soft force sensor. The sensitivity of the proposed multi-axis force sensor has been characterized in x, y and z directions. Experimental results show that the sensor can achieve 2.1%/N and 2.2%/N in x and y axis, and 4.2%/N in z axis, respectively. The force sensor has been implemented in dexterous manipulations with a robotic gripper as the proof-of-concept, which demonstrates great potential for multi-axis force sensing applications.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".