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Self-optimizing process planning of multi-step polishing processes
- Source :
- Production Engineering. 15:563-571
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Self-optimizing process planning is an essential approach for finding optimum process parameters and reducing ramp-up times in machining processes. For this purpose, polishing is presented as an application example. In conventional polishing processes, the process parameters are selected according to the operator’s expertise in order to achieve a high-quality surface in the final production step. By implementing machine learning (ML) models in process planning, a correlation between process parameter and measured surface quality is generated. The application of this knowledge automates the selection of optimal process parameters in computer-aided manufacturing (CAM) and enables a continuous adaptation of the NC-code to changing process conditions. Applying the presented ML-model, the prediction accuracy of 83% will adapt the process parameters to achieve the target roughness of 0.2 μm. The sample efficiency is shown by the decrease in root mean square error from 0.1–0.28 to 0.02–0.07 μm with additional polishing iterations.
- Subjects :
- 0209 industrial biotechnology
Mean squared error
business.industry
Computer science
Mechanical Engineering
Process (computing)
Polishing
02 engineering and technology
Surface finish
Process variable
Work in process
Industrial and Manufacturing Engineering
020303 mechanical engineering & transports
020901 industrial engineering & automation
0203 mechanical engineering
Machining
Process engineering
business
Adaptation (computer science)
Subjects
Details
- ISSN :
- 18637353 and 09446524
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Production Engineering
- Accession number :
- edsair.doi...........944bf635002c864aad68cbdcc00d0815