Design optimization and simulation of a 3D printed cable-driven continuum robot using IKM-ANN and nTop software
Bibliographic record
Abstract
The first aspect of the paper focuses on presenting the innovative design of a new continuum robot, which was initially conceptualized using SolidWorks and then brought to life through 3D printing. This section illustrates the construction process, detailing the wiring method and the separator between each section of the robot. A key feature of the newly proposed design is the ball-like shape on the upper side of each disk, allowing each disk to rotate freely and gracefully in conjunction with the next one. To further enhance the design, the disk was optimized using nTopology software, an AI-based solution that reduces weight while maintaining performance. This modern engineering tool proved to be instrumental in addressing engineering challenges effectively. Subsequently, both the original and optimized disks were fabricated using 3D printing technology. In addition to the physical construction, the study employed an Artificial Neural Network (ANN) coupled with Particle Swarm Optimization (PSO) to simulate the developed model by solving its inverse kinematic model. The findings from this research have paved the way for a new continuum robot design that can be trained using the ANN-PSO method. Furthermore, the powerful nTopology tool was demonstrated to be capable of skillfully optimizing any given components without sacrificing performance.
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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".