Effects of DC-powered pistol grip tool location and orientation on operator upper extremity stiffness and damping
Bibliographic record
Abstract
In automotive assembly lines, workers routinely used DC-powered pistol grip tools for the installation of threaded fasteners. The stiffness and damping offered by the hand-tool system dictates the handle displacement due to the reaction torque. The aim of the study was to predict the typical ranges of stiffness and damping offered by the upper extremity in different wrist orientations and locations while operating a pistol-grip hand tool. The hand-tool system was represented using a single degree-of-freedom torsional model and a deterministic approach was adopted to identify the system parameters. Tightening tasks were executed by ten experienced hand-tool operators at three torque levels (5 Nm, 7.5 Nm, and 10 Nm) and at four different fastener locations corresponding to varying wrist orientations. At 5 Nm, 7.5 Nm, and 10 Nm torques, the mean operator stiffnesses were 645 N/m, 879.5 N/m, and 1019 N/m respectively with a mean damping being 22.88 N/m, 15.14 N/m and 12.38 N/m respectively. The stiffness coefficients were different between wrist positions but not the damping coefficients. The research demonstrates the approach to model pistol grip hand tool operation and determine the stiffness and damping parameters. This approach could be used for determining optimal torque ranges and positions to minimize rotary tool handle displacement due to reaction torque, thereby reducing the risk of injury.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".