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.