Slicer based trajectory planning for 3D printing with novel kinematically redundant (6+3)-DOF parallel robots
Bibliographic record
Abstract
Additive manufacturing (3D printing) has evolved from a prototyping tool into a mainstream production technology capable of creating complex, customized parts. However, printing freeform or overhanging geometries without support structures remains a major challenge. Support generation increases material use, post-processing effort, and limits the design freedom that defines the promise of 3D printing. Conventional serial robots and gantry printers are constrained by cumulative joint errors, limited orientation range, and discontinuous trajectories. Parallel robotic systems offer greater stiffness and positional accuracy by distributing loads among multiple actuators, yet they suffer from restricted rotational workspace and complex singularity behaviour. Addressing these limitations is essential for enabling multi-axis, support-free manufacturing. This research develops and integrates the hardware and software foundations for robotic 3D printing using two complementary platforms: a novel kinematically redundant (6+3)-DOF gantry-type robot developed for modeling and trajectory planning, and a kinematically redundant (6+3)-DOF Gosselin-style parallel robot used for simulation and framework validation. A unified framework was implemented that includes (i) inverse-kinematic and Jacobian-based formulations for both robots, (ii) a cubic-polynomial trajectory planner with a Jacobian-aware cost function to ensure smooth, feasible motion, and (iii) a Python-based slicer that converts digital models into joint-space trajectories. Simulation studies verified the continuity, constraint satisfaction, and feasibility of the generated paths for both planar and non-planar geometries. Hardware implementation validated extrusion control and pilot printing using a DYZE system with PID-regulated thermal stability. While synchronized, multi-axis support-free printing remains future work, the integrated results confirm the functional readiness of the trajectory-planning and control framework. The principal contribution of this thesis lies in demonstrating how redundancy-aware trajectory planning and custom slicing can bridge the gap between conventional 3D printing and fully support-free, non-planar fabrication. The developed models, software, and verified hardware platform provide a reproducible foundation for future studies in robotic additive manufacturing, trajectory optimization, and precision multi-axis material deposition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".