Smooth and Time-Optimal Trajectory Planning for Multi-Axis Machine Tools
Notice bibliographique
Résumé
This thesis presents novel methods for feedrate optimization and toolpath smoothing in CNC machining. Descriptions of the algorithms, simulation test cases, and experimental results are presented. \nBoth feedrate optimization and toolpath smoothing are essential for increasing manufacturing efficiency while retaining part quality in CNC machining. The application of high-speed machining also necessitates the use of high feedrates, and smooth toolpaths which can be safely traversed at high feeds. \nHowever, problems occur when the feedrate is increased without check. High tracking error in machining may cause part tolerance errors. Transient vibrations due to jerky movement can lead to poor part surface quality. High speed trajectories may also demand greater torque than what the feed drives are capable of producing, which affects the motion controller’s ability to follow the trajectory correctly. The condition of the machine is also a concern, with the potential for damage or excessive wear on the machine’s components, if excessive axis velocity or jerk (i.e., rate of change of acceleration) is commanded. \nThe feedrate scheduling algorithm developed in this thesis combines linear and nonlinear programming in a dual-windowed implementation. Linear programming (which is computationally fast) is used to quickly provide a near-optimal guess, based on axis velocity, acceleration, and jerk constraints. The solution is then refined through the use of nonlinear optimization. In the latter step, requiring more computations, the commanded motor torque and expected servo error are constrained directly, leading to shorter movement time. A windowing alignment procedure is presented which allows for these two optimization methods, each with different problem constraints and solutions horizons, to work in tandem with one another without risking infeasible boundary conditions between the windows. The algorithm is validated in simulation and experiment studies. Case studies analyzing the parameters of the optimization algorithm are also presented, and the configuration which is most computationally efficient is determined. \nA toolpath generation method is presented in which Euler-spiral pairs are used to smooth sharp corners, with an algorithm that integrates directly with the developed feedrate optimization The result is an exactly arc-length parametrized, G2-continuous toolpath whose axis derivatives can be computed very efficiently, which helps reduce the overall computation time. \nA repositioning toolpath method is also developed to reduce the cycle time of multi-layer contouring operations. This method replaces circular arc based repositioning segments between contouring passes (commonly used in industry) with a smooth Euler spiral based curve. This avoids tangent and curvature discontinuities, allowing for smoother motion with lower velocity and acceleration demands, while also reducing the overall motion. The repositioning toolpath has also been integrated with feedrate optimization and validated in simulation results.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».