Ontology-Based Skill Description Learning for Flexible Production Systems
| Autor: | Anna Himmelhuber, Thomas A. Runkler, Sonja Zillner, Stephan Grimm |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning business.industry Computer science Computer Science - Artificial Intelligence 02 engineering and technology Ontology (information science) Machine Learning (cs.LG) 020901 industrial engineering & automation Production planning Artificial Intelligence (cs.AI) Inductive logic programming 0202 electrical engineering electronic engineering information engineering Production (economics) 020201 artificial intelligence & image processing Software engineering business Semantic Web |
| Zdroj: | ETFA |
| DOI: | 10.48550/arxiv.2111.13142 |
| Popis: | The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize this potential of dynamic manufacturing through automatic production planning, formal skill descriptions of the machines are essential. However, generating those skill descriptions in a manual fashion is labor-intensive and requires extensive domain-knowledge. In this contribution an ontology-based semi-automatic skill description system that utilizes production logs and industrial ontologies through inductive logic programming is introduced and benefits and drawbacks of the proposed solution are evaluated. Comment: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) |
| Databáze: | OpenAIRE |
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