Hybrid Artificial Intelligence System for the Design of Highly-Automated Production Systems
| Autor: | Atakan Sünnetcioglu, Simon Hagemann, Rainer Stark |
|---|---|
| Přispěvatelé: | Publica |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Artificial Intelligence System Computer science business.industry Automotive industry Core competency 02 engineering and technology Industrial and Manufacturing Engineering Field (computer science) Manufacturing engineering Product (business) 020303 mechanical engineering & transports 020901 industrial engineering & automation Software 0203 mechanical engineering Work (electrical) Artificial Intelligence Production (economics) ddc:620 business |
| Zdroj: | Procedia Manufacturing. 28:160-166 |
| ISSN: | 2351-9789 |
| DOI: | 10.1016/j.promfg.2018.12.026 |
| Popis: | The automated design of production systems is a young field of research which has not been widely explored by industry nor research in recent decades. Currently, the effort spent in production system design is increasing significantly in automotive industry due to the number of product variants and product complexity. Intelligent methods can support engineers in repetitive tasks and give them more opportunity to focus on work which requires their core competencies. This paper presents a novel artificial intelligence methodology that automatically generates initial production system configurations based on real industrial scenarios in the automotive field of body-in-white production. The hybrid methodology reacts flexibly against data sets of different content and has been implemented in a software prototype. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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