Abstract:
Product Disassembly has become an area of active research as
it supports sustainable development by aiding effective endof-life (EOL) stage strategies like reuse, re-manufacturing, recycling, etc. In this work, we propose a new approach, 3DDSPNet, that can utilize 3D data from CAD assembly models
to generate a feasible disassembly sequence. Our approach
uses Graph-based learning to process the graph representation of CAD models. Currently, the available 3D CAD model
datasets lack ground truth disassembly sequences. We propose and curate a new dataset, the 3D-DSP dataset, which
includes ground truth information about the disassembly sequence for 3D product models. We carry out evaluation and
analysis of results to explain the efficacy of the proposed
method. Our approach significantly outperforms the existing
baseline. We develop an Autodesk Fusion 360 plug-in that
generates disassembly sequence animation, allowing intuitive
analysis of the disassembly plan.
