dc.identifier.uri |
http://dx.doi.org/10.15488/1829 |
|
dc.identifier.uri |
http://www.repo.uni-hannover.de/handle/123456789/1854 |
|
dc.contributor.author |
Brüning, J.
|
|
dc.contributor.author |
Denkena, Berend
|
|
dc.contributor.author |
Dittrich, Marc-André
|
|
dc.contributor.author |
Hocke, T.
|
|
dc.date.accessioned |
2017-09-07T11:22:18Z |
|
dc.date.available |
2017-09-07T11:22:18Z |
|
dc.date.issued |
2017 |
|
dc.identifier.citation |
Brüning, J.; Denkena, B.; Dittrich, M.-A.; Hocke, T.: Machine Learning Approach for Optimization of Automated Fiber Placement Processes. In: Procedia CIRP 66 (2017), S. 74-78. DOI: https://doi.org/10.1016/j.procir.2017.03.295 |
|
dc.description.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. |
eng |
dc.description.sponsorship |
BMWi/ZIM KF2328125PO4 |
|
dc.language.iso |
eng |
|
dc.publisher |
Amsterdam : Elsevier |
|
dc.relation.ispartofseries |
Procedia CIRP 66 (2017) |
|
dc.rights |
CC BY-NC-ND 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
dc.subject |
Assisted process planning |
eng |
dc.subject |
Machine learning |
eng |
dc.subject |
Process data visualization |
eng |
dc.subject |
Artificial intelligence |
eng |
dc.subject |
Automation |
eng |
dc.subject |
Carbon |
eng |
dc.subject |
Carbon fiber reinforced plastics |
eng |
dc.subject |
Carbon fibers |
eng |
dc.subject |
Composite materials |
eng |
dc.subject |
Data visualization |
eng |
dc.subject |
Fiber reinforced plastics |
eng |
dc.subject |
Manufacture |
eng |
dc.subject |
Process monitoring |
eng |
dc.subject |
Automated generation |
eng |
dc.subject |
Carbon fibre reinforced polymer |
eng |
dc.subject |
Economic potentials |
eng |
dc.subject |
Machine learning approaches |
eng |
dc.subject |
Manufacturing knowledge |
eng |
dc.subject |
On-line process monitoring |
eng |
dc.subject |
Process data visualization |
eng |
dc.subject |
Quality inspection |
eng |
dc.subject |
Learning systems |
eng |
dc.subject.ddc |
620 | Ingenieurwissenschaften und Maschinenbau
|
ger |
dc.title |
Machine Learning Approach for Optimization of Automated Fiber Placement Processes |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.issn |
00136848 |
|
dc.relation.doi |
https://doi.org/10.1016/j.procir.2017.03.295 |
|
dc.bibliographicCitation.volume |
66 |
|
dc.bibliographicCitation.firstPage |
74 |
|
dc.bibliographicCitation.lastPage |
78 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|