Dystamill: a framework dedicated to the dynamic simulation of milling operations for stability assessment

Huynh, Hoai Nam; Rivière-Lorphèvre, Édouard; Ducobu, François; Özcan, Abdullah; Verlinden, Olivier

doi:10.1007/s00170-018-2357-3

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…The coefficients K t/r/a,c and K t/r/a,e are experimentally identified [25,26]. The amount of slices recommended for correct estimation of the cutting forces is computed following the indications given in [4]. Finally, as an illustration of 5-axis operations, a test case is considered where a ball-end tool runs alongside a 5-axis path, shaping a Al6060 aluminium block into a portion of cone.…”

Section: Cutter-workpiece Engagementmentioning

confidence: 99%

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Modelling of cutter-workpiece engagement with tri-dexel approach for dynamic simulations

Dambly

Rivière-Lorphèvre²,

Ducobu³

et al. 2022

Preprint

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In the context of Industry 4.0, the modelling of machining operations happens to be a crucial aspect of production sector. With adequate models, it is possible to predict the appearance of chatter and select optimised operational parameters. For the dynamics simulation of machine tools or robots performing 5-axis operations, modelling approaches are continuously in improvement. A robust method is proposed for the computation of the cutter-workpiece engagement (CWE) at each step of a dynamic simulation for the determination of the machining forces and the resulting machined surface. The CWE is estimated based on the interference between the workpiece, modelled with tri-dexel approach, and the tool, considered as a triangle-mesh swept volume. The relative-closest triangle algorithm is used for a robust intersection management with 5-axis trajectories. A hybrid dexel-based-analytic method is presented for accurate estimation of the uncut chip thickness. Furthermore, an approach is proposed for a simulation-based evaluation of the part resulting from dynamic simulations by comparing dexel networks with each other, allowing to assess the impact of operational parameters on the parts. The proposition is validated through experimental and virtual data with forces measurements as well as benchmark tests.

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Section: Cutter-workpiece Engagementmentioning

confidence: 99%

“…There are plenty of modelling approaches for such simulations. Typically, for 2.5D milling operations, the workpieces are modelled as a stack of slices [3,4].…”

Section: Introductionmentioning

confidence: 99%

Modelling of cutter-workpiece engagement with tri-dexel approach for dynamic simulations

Dambly

Rivière-Lorphèvre²,

Ducobu³

et al. 2022

Preprint

Self Cite

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show abstract

“…The forces are then applied on the TCP. Their computation is the result of the coupling of two simulators, on the one hand EasyDyn, an in-house multi-body solver and on the other hand DyStaMill, an in-house solver as well, dedicated to the simulation of machining operations [13].…”

Section: Multibody Dynamic Modelmentioning

confidence: 99%

“…However, the complexity of voxel model is up to O(n 3 ) against O(n 2 ) for dexel. Besides, for 2.5D operations, the stack of slices approach is faster and reliable [13].…”

Section: Introductionmentioning

confidence: 99%

Coupled Multibody Model Of Industrial Robot With Milling Simulator For Trajectory Compensation

Dambly¹,

Huynh²,

Verlinden³

et al. 2021

Proceedings of the 10th ECCOMAS Thematic Conference on MULTIBODY DYNAMICS

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Robotic machining is a fast-growing technology in the field of mechanical manufacturing. Indeed, it is generally accepted that for the same working space, a fully equipped robotic machining cell can cost 30 to 50 % less than a conventional machine tool. However, inaccuracies resulting either from vibrations or deflections occur while the robot is subjected to cutting forces, inherent to its flexible structure. As an order of magnitude, the stiffness at the tool-tip is about 1N/µm for industrial robots against more than 50N/µm for CNC machine tools. The flexibility source has been investigated and appears to be caused by the robot articulations in a proportion of 80% while the remaining flexibility issues from the structural elasticity. In order to improve the accuracy of robotic machining operations, several approaches have been carried out such as the study of stable cutting conditions and the online/offline compensation of the tool trajectory.Two aspects of the operation must be modeled, on the one hand the model of the cutting machine, being an industrial robot in robotic machining, and on the other hand, the machining model including the resulting geometry of the workpiece. A coupled model is then proposed with the multi-body model of the robot subjected to machining forces. The multi-body model includes the flexibility induced by the structure and the articulations. In order to compensate the deviations, a solution is proposed where the trajectory is discretized in nodes with a compensation taking the system dynamics into account by successive simulations of the operation. The algorithm involves two steps, firstly it aims to detect critical locations of the path and add or reposition nodes to reduce the deviation and secondly an optimization layer modifies nodes positions and velocities for a finer reduction. The method is deployed for three systems of increasing complexity for a face milling operation, showing a machining error reduction.

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Tri-dexel-based cutter-workpiece engagement: computation and validation for virtual machining operations

Dambly

Rivière-Lorphèvre

Ducobu

et al. 2023

Int J Adv Manuf Technol

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Dystamill: a framework dedicated to the dynamic simulation of milling operations for stability assessment

Cited by 6 publications

References 16 publications

Modelling of cutter-workpiece engagement with tri-dexel approach for dynamic simulations

Modelling of cutter-workpiece engagement with tri-dexel approach for dynamic simulations

Coupled Multibody Model Of Industrial Robot With Milling Simulator For Trajectory Compensation

Tri-dexel-based cutter-workpiece engagement: computation and validation for virtual machining operations

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