Development of an adaptive, self-learning control concept for an additive manufacturing process
| Autor: | Arne Neef, Claus Emmelmann, Volker Renken, Gert Goch, Stephan Albinger |
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| Přispěvatelé: | Publica |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering 010308 nuclear & particles physics business.industry Control (management) Stability (learning theory) Process (computing) Control engineering 02 engineering and technology 01 natural sciences Industrial and Manufacturing Engineering 020901 industrial engineering & automation Optical path Cascade 0103 physical sciences Production (economics) Laser power scaling Selective laser melting business |
| Zdroj: | CIRP Journal of Manufacturing Science and Technology. 19:57-61 |
| ISSN: | 1755-5817 |
| DOI: | 10.1016/j.cirpj.2017.05.002 |
| Popis: | Error avoidance in high-precision manufacturing processes becomes more important for numerous state-of-the-art technologies. Selective laser melting is one of these technologies offering large potentials in the production of complex and flexible metal products. As the technology is relatively new, it is vulnerable for errors, given that the process parameters are not measured yet. A novel multilevel control concept, incorporating several sensors, has the potential to reduce errors significantly. For inner cascade control, the laser power will be adjusted by measurements with an intensity sensor for wavelengths in the visible range. This sensor is integrated into the optical path of the laser beam. An adapted self-learning strategy supports the stability of the process by updating the parameters of the used multidimensional model in order to attenuate environmental influences or shifts within the process. This work presents the concept of the control approach, first measurement results and the required relations between measurement, process and control parameters. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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