Supporting Skill-based Flexible Manufacturing with Symbolic AI Methods

Many manufacturing processes ask for greater flexibility and transparency, which represent key features for constructing robust industrial cyber-physical systems. In the Industry 4.0 scenario, where factories are equipped with 'smart' machines, it is crucial to have robust AI methods that allow to completely automate assembly by quickly adapting to various production requests. This is complemented by making the system transparent, which gives understanding how the manufacturing process is carried out and helps build trust in such systems. To this end, we exploit semantic technologies to represent machines' capabilities and match them to production requests, to address the question of flexibility, as well as AI planning techniques to generate production sequences. We utilize OWL justifications, as a well established explanations technique in OWL ontologies, to explain the matching of machines' skills to product requests and the generated production sequences. We illustrate results from our methods for an experimental production facility.