AI & ML
Fastener-centric acquisition and AND/OR graph representation of state-dependent disassembly knowledge from CAD-derived virtual assemblies
arXiv cs.RO0 views
arXiv:2611.13379v1 Announce Type: new Abstract: Design-stage disassemblability assessment requires knowledge of product structure and the operations available in each assembly state, but CAD assemblies do not directly specify state-dependent disassembly operations. This paper proposes a fastener-centric virtual-disassembly framework that acquires such knowledge from CAD-derived virtual assemblies. Fasteners jointly releasing the same constraint are grouped into operation-level sets, avoiding enumeration of arbitrary individual-fastener orders. Their removability is evaluated through state-dependent visibility and tool-access checks and torque-driven virtual-removal trials. After fastener removal, graph-based reasoning identifies candidate moving units, whose separation is evaluated by initial-motion screening, path generation, and physics-based validation. The acquired operations, states, dependencies, validated paths, and simulation-derived attributes are organized into a Transformed AND/OR Graph for subsequent planning and design evaluation. Exhaustive verification with a controlled toy assembly found no branching violations for introduced tool-accessibility and precedence conditions. Evaluation with a 54-component OpenManipulator Chain gripper demonstrated state-dependent fastener-removal and part- or subassembly-separation operations. Visibility and tool-accessibility evaluations pruned 51.4% of fastener-set candidate evaluations, while removal of obstructing components enabled previously inaccessible fastener operations. Operation-level grouping and screening thereby limited detailed physical validation to mechanically plausible candidates. The results demonstrate the feasibility of acquiring product-specific disassembly knowledge from CAD-derived virtual assemblies before manufacture, supporting subsequent disassembly reasoning and design-for-disassembly evaluation.
Source:https://arxiv.org/abs/2611.13379PreviousAHEADance: Anticipatory Humanoid Dance from Streaming AudioNextDiscrete Model-Based Diffusion for Combinatorial Optimization in Multi-Robot Task Allocation
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