Details
| Original language | English |
|---|---|
| Pages (from-to) | 74-78 |
| Number of pages | 5 |
| Journal | Procedia CIRP |
| Volume | 66 |
| Publication status | Published - 6 Jun 2017 |
| Event | 1st CIRP Conference on Composite Materials Parts Manufacturing, CIRP CCMPM 2017 - Karlsruhe, Germany Duration: 8 Jun 2017 → 9 Jun 2017 |
Abstract
Automated Fiber Placement (AFP) processes are commonly deployed in manufacturing of lightweight structures made of carbon fibre reinforced polymer. In general, AFP is connected to individual manufacturing knowledge during process planning and time consuming manual quality inspections. In both cases, automatic solutions provide a high economic potential. Therefore, a machine learning approach for planning, optimizing and inspection of AFP processes is presented. Process data from planning, CNC and online process monitoring is aggregated for the documentation of the part specific manufacturing history and the automated generation of manufacturing knowledge. Within this approach a complete automation of data capturing, data storing, modeling and optimizing is achieved.
Keywords
- Assisted process planning, Machine learning, Process data visualization
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 66, 06.06.2017, p. 74-78.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Machine Learning Approach for Optimization of Automated Fiber Placement Processes
AU - Brüning, J.
AU - Denkena, B.
AU - Dittrich, M. A.
AU - Hocke, T.
PY - 2017/6/6
Y1 - 2017/6/6
N2 - Automated Fiber Placement (AFP) processes are commonly deployed in manufacturing of lightweight structures made of carbon fibre reinforced polymer. In general, AFP is connected to individual manufacturing knowledge during process planning and time consuming manual quality inspections. In both cases, automatic solutions provide a high economic potential. Therefore, a machine learning approach for planning, optimizing and inspection of AFP processes is presented. Process data from planning, CNC and online process monitoring is aggregated for the documentation of the part specific manufacturing history and the automated generation of manufacturing knowledge. Within this approach a complete automation of data capturing, data storing, modeling and optimizing is achieved.
AB - Automated Fiber Placement (AFP) processes are commonly deployed in manufacturing of lightweight structures made of carbon fibre reinforced polymer. In general, AFP is connected to individual manufacturing knowledge during process planning and time consuming manual quality inspections. In both cases, automatic solutions provide a high economic potential. Therefore, a machine learning approach for planning, optimizing and inspection of AFP processes is presented. Process data from planning, CNC and online process monitoring is aggregated for the documentation of the part specific manufacturing history and the automated generation of manufacturing knowledge. Within this approach a complete automation of data capturing, data storing, modeling and optimizing is achieved.
KW - Assisted process planning
KW - Machine learning
KW - Process data visualization
UR - http://www.scopus.com/inward/record.url?scp=85027588868&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2017.03.295
DO - 10.1016/j.procir.2017.03.295
M3 - Conference article
AN - SCOPUS:85027588868
VL - 66
SP - 74
EP - 78
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 1st CIRP Conference on Composite Materials Parts Manufacturing, CIRP CCMPM 2017
Y2 - 8 June 2017 through 9 June 2017
ER -