Optimization Acceleration and Contact Force of Space Slider-Crank Mechanism with Spherical Clearance Joints
Bibliographic record
Abstract
Clearance always exists in revolute kinematic joints and spherical joints due to manufacturing, assembly, wear, etc., Proper clearance helps the mechanism operate smoothly.However, the friction inside the joint causes the joint clearance to increase, resulting in mechanical vibrations.These are demonstrated through the analysis of the dynamics of the spatial slider crank mechanism using Rigid dynamics.in ANSYS.To ensure smooth operation of the space slider crank mechanism.It is necessary to select the length of the crank, the revolute and ball joint clearances, the friction coefficient inside the kinematic joint and the crank driving speed.Because these design parameters all affect the slider acceleration and the contact force within revolute and clearance ball joints.To do this, the Grey-Taguchi method is proposed.From the results of the rigid dynamics analysis of the spatial slider crank mechanism, it has been proven that increasing the design variables causes the acceleration and contact force to increase significantly, causing instability for the crank mechanism.space slider.The grey relational analysis -Taguchi optimization results also confirm this.The results of grey relational Analysis-Taguchi method achieved the optimal acceleration of the slider and the optimal contact force in the revolute joint of the space slider crank mechanism being 32.72 m/s 2 and 1.3452 kN, respectively.To increase the reliability of this optimization method, decision-making methods with multiple criteria or multiple objectives are also applied, such as the TOPSIS method, the SAW method and the WASPAS method.The results of these methods confirm the same results as the Grey-Taguchi method.The optimal results were chosen the space slider crank mechanism model with crank size, friction coefficient, revolute joint and ball joint clearance sizes and crank driving speed 80 mm, 0.01, 0.1 mm and 800 rpm, respectively.
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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".