Identification of Lap Joint Dynamics Using Interfacial Nanocomposite Force Sensor
Bibliographic record
Abstract
Machine tools are complex structures consisting of several parts connected through different types of joints. Mechanical joints affect dynamics of the machine tools significantly, and any virtual model of the structure should include joint properties. It is desirable to use an interfacial sensor inside the joint to directly identify joint dynamic properties without changing the joint’s design and dynamics. In this study, a polymeric nanocomposite sensor with high sensitivity and a wide frequency bandwidth is implemented inside a bolted lap joint to identify the joint dynamic properties. The sensor implementation does not require any modifications to the joint design which makes the proposed approach suitable for many applications. An identification procedure is developed to find the micro-slip regime and the stick-slip boundaries in the joint interface using the acquired data of the nanocomposite sensor. Lab scale experiments are then conducted on a structure that consists of two beams attached to each other using a bolted lap joint. The proposed method is then used to identify the joint dynamics and the results are then compared with an existing approach named hysteresis loop technique. The experimental results show that the proposed method can predict the joint properties effectively with maximum deviations of 17% compared to the hysteresis loop results. Furthermore, effects of the contact normal load and the excitation load on the joint properties are investigated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".