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.

Developing neural networks for supervised multi-class classification has become important for theory and practice. An essential point is the design of the underlying network. Beside single-network approaches there are several multi-class patterns which decompose a classification problem into multiple sub-problems and derive systems of neural networks. We show that existing multi-class patterns can be improved by a new and simple labeling scheme for the training of the sub-problems. We efficiently derive a class hierarchy which is optimized for our labeling scheme and, unlike most of existing works, has no schematic restrictions. Based on that we introduce a hierarchical multi-class pattern, called ConfusionTree-pattern, which is able to reach high classification accuracies. Our experiments show that our multi-class ConfusionTree-pattern reaches state-of-the-art results regarding performance and efficiency.