Increased Reliability of Draw-In Prediction in a Single Stage Deep-Drawing Operation via Transfer Learning
Publikation: Beiträge in Zeitschriften › Konferenzaufsätze in Fachzeitschriften › Forschung › begutachtet
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in: Procedia CIRP, Jahrgang 130, 01.01.2024, S. 270-275.
Publikation: Beiträge in Zeitschriften › Konferenzaufsätze in Fachzeitschriften › Forschung › begutachtet
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TY - JOUR
T1 - Increased Reliability of Draw-In Prediction in a Single Stage Deep-Drawing Operation via Transfer Learning
AU - Wollschlaeger, Lea
AU - Heinzel, Christine
AU - Thiery, Sebastian
AU - Abdine, Mazhar Zein El
AU - Khalifa, Noomane Ben
AU - Heger, Jens
N1 - Conference code: 57
PY - 2024/1/1
Y1 - 2024/1/1
N2 - In sheet metal forming operations, finite element simulations and experimental works are used to evaluate predictions on different parameter settings. During the manufacturing process, there often exists a discrepancy of expected outcomes due to varying material properties. With the aim to save simulation and experimental resources, this paper provides a reliable transfer learning model suiting the deep-drawing case, where a model is pre-trained on simulation data, neurons are frozen in different layers and is then fine-tuned on real data. This model is evaluated in its behavior by gradually learning on different number of real data points as well as simulation data points.
AB - In sheet metal forming operations, finite element simulations and experimental works are used to evaluate predictions on different parameter settings. During the manufacturing process, there often exists a discrepancy of expected outcomes due to varying material properties. With the aim to save simulation and experimental resources, this paper provides a reliable transfer learning model suiting the deep-drawing case, where a model is pre-trained on simulation data, neurons are frozen in different layers and is then fine-tuned on real data. This model is evaluated in its behavior by gradually learning on different number of real data points as well as simulation data points.
KW - Artificial Intelligence
KW - Machine Learning
KW - Artificial neural network
KW - Transfer Learning
KW - Industrial applications
KW - Deep-drawing
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85213035295&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2024.10.086
DO - 10.1016/j.procir.2024.10.086
M3 - Conference article in journal
VL - 130
SP - 270
EP - 275
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 57th CIRP Conference on Manufacturing Systems - CIRP CMS 2024
Y2 - 29 May 2024 through 31 May 2024
ER -