The automatic generation of (dis)assembly sequences for complex technical products is a challenging field. Complex products like vehicles consist of numerous different components. Determining the sequence using a brute-force-approach by testing all components for disassembly one after another in a loop until all components are disassembled is laborious and costly. In industrial scenarios, a large proportion of the components are fasteners. In this paper, we propose a new framework which improves the disassembly sequencing generation by prioritizing fasteners during planning. Our proposed framework comprises a preprocessing in which fasteners are identified with a convolutional neural network within a dataset and a procedure that preferentially and automatically checks fasteners for disassembly. The algorithm takes initial and unavoidable collisions of the fasteners into account. We show the effectiveness of our approach on real-world data from the automotive industry. A new synthetic dataset of fasteners for training neural networks is available.

Automatic disassembly planning for complex industrial products like vehicles checks the expandability of components already at early stages of design. For a fast computation of collision-free disassembly paths, sampling-based rigid body motion planning is used in the literature. However, in real-world scenarios there are circumstances that prevent the finding of plausible collision-free disassembly paths with these conventional motion planners. The most difficult problem is that many components have deformable fastening elements that are modeled in a relaxed state and often as a part of the rigid object. The fastening elements cause unavoidable collisions of the component with its environment along the actual disassembly path. In this paper, we present Iterative Mesh Modification Planning (IMMP). Given the information about fastening elements in advance, our method applies a controlled iterative process of geometric deformations and planning attempts to the component to be disassembled. With this process, we are able to disassemble the component from its installed position with a conventional rigid body motion planner taking fastening elements and also overpressure into account. We demonstrate the effectiveness of our method on real-world planning scenarios from the automotive industry.

In this paper, we consider disassembly scenarios for real-world 3D CAD-data, where each component is defined by a triangle mesh. For a fast construction of collision-free disassembly paths, common approaches use sampling-based rigid body motion planning which is well studied in the literature. One fact that has so far received little attention is that in industrial disassembly scenarios components are often attached to each other with flexible fastening elements like clips. In the planning process, the fastening elements show the following characteristics: 1) They can cause complex non-linear disassembly paths. 2) They are often deformable. 3) They are usually modeled in a relaxed state and as an unknown part of the rigid mesh. That leads to the problem that unavoidable collisions occur during the planning process. Hence, the localization of the fastening elements and the integration of this information into the motion planning process is crucial for an automatic disassembly.We present a new geometric solution to extract salient features of 3D meshes which is specialized to find the fastening elements within the otherwise rigid mesh. Our approach measures a vertex-based surface feature using a local Gauss map in combination with a local thickness computation of the mesh. We compare our surface feature to state-of-the-art mesh saliency methods on various examples. Further, we integrate this measure of per-vertex saliency into a motion planning process and demonstrate the effectiveness of our result on real-world planning scenarios from the automotive industry.