Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach | Paper Citation Record

Observation b2c38318-f2bd-4c88-9c27-b34ae3b9cdf5 · outbound

This paper cites Advanced Materials Research 1175, 123–136 (2023).

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Advanced Materials Research 1175, 123–136 (2023)

Reference 1

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Observation ec3a62fc-83e4-4732-bec8-0fb7bb164779 · outbound

This paper cites teil i, ii und iii.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach teil i, ii und iii

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.497158Z

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This paper cites an unresolved cited work.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:34:36.480032Z

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Observation fd95fb3a-d76a-4259-92bd-f697365e5e8b · outbound

This paper cites an unresolved cited work.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:34:36.456789Z

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Observation 56bbeed2-aeb3-4c65-acc1-631059388b41 · outbound

This paper cites International Journal of Machine Tool Design and Research 22(4), 293–307 (1982) https://doi.org/10.1016/0020-7357(82)90007-5.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach International Journal of Machine Tool Design and Research 22(4), 293–307 (1982) https://doi.org/10.1016/0020-7357(82)90007-5

Reference 5

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.917450Z

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Observation f1cf3e8c-7c57-419a-8379-12948de174e8 · outbound

This paper cites International Journal of Machine Tools and Manufacture 27(1), 1–14 (1987) https://doi.org/10.1016/S0890-6955(87)80035-4.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach International Journal of Machine Tools and Manufacture 27(1), 1–14 (1987) https://doi.org/10.1016/S0890-6955(87)80035-4

Reference 6

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.904910Z

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This paper cites Journal of Materials Research and Technology 30, 4625–4644 (2024) https://doi.org/10.1016/j.jmrt.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Materials Research and Technology 30, 4625–4644 (2024) https://doi.org/10.1016/j.jmrt

Reference 7

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Observation 63167907-3b7a-4ac3-bf1e-d14b84a2fc1d · outbound

This paper cites International Journal of Mechanical Sciences 27(10), 643–651 (1985) https://doi.org/10.1016/ 0020-7403(85)90046-3.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach International Journal of Mechanical Sciences 27(10), 643–651 (1985) https://doi.org/10.1016/ 0020-7403(85)90046-3

Reference 8

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:34:36.436907Z

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Observation c32f9316-15c6-433f-973d-bafc4e4e6e0f · outbound

This paper cites Journal of Materials Processing Technology 140(1), 530–534 (2003) https://doi.org/10.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Materials Processing Technology 140(1), 530–534 (2003) https://doi.org/10

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.413438Z

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This paper cites Journal of Manufacturing Science and Engineering-transactions of The Asme - J MANUF SCI ENG 130 (2008) https://doi.org/10.1115/1.2783273.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Manufacturing Science and Engineering-transactions of The Asme - J MANUF SCI ENG 130 (2008) https://doi.org/10.1115/1.2783273

Reference 10

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.882241Z

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Observation d2b8be0f-2035-4ac3-8663-1b42bac8f0d4 · outbound

This paper cites International Journal of Material Forming 17 (2023) https://doi.org/10.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach International Journal of Material Forming 17 (2023) https://doi.org/10

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.397677Z

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Observation ddc16f63-edd0-4e67-b752-23aefebebbe4 · outbound

This paper cites International Journal of Impact Engineering 166, 104240 (2022) https://doi.org/10.1016/j.ijimpeng.2022.104240.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach International Journal of Impact Engineering 166, 104240 (2022) https://doi.org/10.1016/j.ijimpeng.2022.104240

Reference 12

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This paper cites International Journal of Machine Tools and Manufacture 45(4), 467–479 (2005) https://doi.org/10.1016/ j.ijmachtools.2004.09.007.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach International Journal of Machine Tools and Manufacture 45(4), 467–479 (2005) https://doi.org/10.1016/ j.ijmachtools.2004.09.007

Reference 13

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:34:36.382543Z

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Observation a682cfbb-836e-4f3e-a081-2889deee6203 · outbound

This paper cites Journal of Intelligent Manufacturing 29(5), 1045–1061 (2018) https://doi.org/10.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Intelligent Manufacturing 29(5), 1045–1061 (2018) https://doi.org/10

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.366843Z

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This paper cites Journal of Manufacturing Systems 64, 657–667 (2022) https://doi.org/10.1016/j.jmsy.2022.04.011 35.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Manufacturing Systems 64, 657–667 (2022) https://doi.org/10.1016/j.jmsy.2022.04.011 35

Reference 15

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.870837Z

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Observation 3f1d4ba2-3384-4553-a72b-e142ca70bc45 · outbound

This paper cites Journal of Manufacturing Processes 89, 458–471 (2023) https://doi.org/10.1016/ j.jmapro.2023.01.078.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Manufacturing Processes 89, 458–471 (2023) https://doi.org/10.1016/ j.jmapro.2023.01.078

Reference 16

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:34:36.352271Z

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This paper cites Journal of Materials Research and Technology 27, 8228– 8243 (2023) https://doi.org/10.1016/j.jmrt.2023.11.193.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Materials Research and Technology 27, 8228– 8243 (2023) https://doi.org/10.1016/j.jmrt.2023.11.193

Reference 17

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.858782Z

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This paper cites Journal of Manufacturing Processes 72, 529–543 (2021) https://doi.org/10.1016/j.jmapro.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Manufacturing Processes 72, 529–543 (2021) https://doi.org/10.1016/j.jmapro

Reference 18

Resolution: malformed identifier; no resolver link, observed 2026-08-12T16:34:35.612388Z

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This paper cites Journal of Intelligent Manufacturing 33(2), 617–635 (2022) https://doi.org/10.1007/ s10845-021-01886-w.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Intelligent Manufacturing 33(2), 617–635 (2022) https://doi.org/10.1007/ s10845-021-01886-w

Reference 19

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:34:36.337127Z

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This paper cites Journal of Intelligent Manufacturing (2023) https://doi.org/10.1007/ s10845-023-02282-2.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Intelligent Manufacturing (2023) https://doi.org/10.1007/ s10845-023-02282-2

Reference 20

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:34:36.322822Z

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Observation 163d4a14-5554-4915-968a-1ba96da6043f · outbound

This paper cites A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations

Reference 21

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This paper cites Computer Methods in Applied Mechanics and Engineering 379, 113741 (2021) https://doi.org/10.1016/j.cma.2021.113741.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Computer Methods in Applied Mechanics and Engineering 379, 113741 (2021) https://doi.org/10.1016/j.cma.2021.113741

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T16:34:35.627022Z

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This paper cites Journal of Computational Physics 404, 109120 (2020).

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Computational Physics 404, 109120 (2020)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.305811Z

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This paper cites Journal of Intelligent Manufacturing 33(1), 259–282 (2022) https://doi.org/10.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Intelligent Manufacturing 33(1), 259–282 (2022) https://doi.org/10

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.290667Z

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This paper cites Production Engineering 17(1), 21–36 (2023) https://doi.org/10.1007/ s11740-022-01150-x.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Production Engineering 17(1), 21–36 (2023) https://doi.org/10.1007/ s11740-022-01150-x

Reference 25

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Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:34:36.258941Z

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This paper cites Jour- nal of Manufacturing Systems 74, 690–702 (2024) https://doi.org/10.1016/j.jmsy.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Jour- nal of Manufacturing Systems 74, 690–702 (2024) https://doi.org/10.1016/j.jmsy

Reference 27

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This paper cites Engineering Applications of Artificial Intelligence 21(8), 1170–1181 (2008) https://doi.org/10.1016/j.engappai.2008.04.001.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Engineering Applications of Artificial Intelligence 21(8), 1170–1181 (2008) https://doi.org/10.1016/j.engappai.2008.04.001

Reference 28

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.833483Z

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Observation 9e4b3e2c-37d0-4603-bb5e-13af15290eba · outbound

This paper cites In: III, H.D., Singh, A.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach In: III, H.D., Singh, A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.243761Z

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Observation a998e743-2ada-4214-b920-cf5f7b06a576 · outbound

This paper cites In: International Conference on Learning Representations (2019).

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach In: International Conference on Learning Representations (2019)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.228365Z

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Observation 7fa7cce0-0de9-46a5-933a-8096f850dd5a · outbound

This paper cites PAMM22(1), 202200306 (2023) https://doi.org/10.1002/pamm.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach PAMM22(1), 202200306 (2023) https://doi.org/10.1002/pamm

Reference 31

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Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Unresolved cited work

Reference 32

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Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Unresolved cited work

Reference 33

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This paper cites In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T16:34:35.683370Z

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This paper cites In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.170560Z

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Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-12T16:34:36.155362Z

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This paper cites Hanser, M¨ unchen (2012).

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Hanser, M¨ unchen (2012)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.139783Z

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Observation c0153e43-36af-4ace-a785-03a0de2171c9 · outbound

This paper cites Springer, ??? (1981).

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Springer, ??? (1981)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.125099Z

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Observation 05109abf-e437-494a-aee2-02a7d85d08dc · outbound

This paper cites Artificial Intelligence Review 56(7), 6295–6364 (2023) https://doi.org/10.1007/s10462-022-10321-2.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Artificial Intelligence Review 56(7), 6295–6364 (2023) https://doi.org/10.1007/s10462-022-10321-2

Reference 39

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This paper cites Journal of Big Data 11(1) (2024) https://doi.org/10.1186/ s40537-023-00876-4.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Journal of Big Data 11(1) (2024) https://doi.org/10.1186/ s40537-023-00876-4

Reference 40

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:34:36.110745Z

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Observation efd4c539-6023-4652-b753-30b686acff2b · outbound

This paper cites International Journal of Machine Tools and Manufacture 49(6), 521–529 (2009) https://doi.org/10.1016/j.ijmachtools.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach International Journal of Machine Tools and Manufacture 49(6), 521–529 (2009) https://doi.org/10.1016/j.ijmachtools

Reference 41

Resolution: malformed identifier; doi_truncated, observed 2026-08-12T16:34:35.804286Z

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This paper cites Materials & Design 32(2), 838–850 (2011) https://doi.org/ 37 10.1016/j.matdes.2010.07.015.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Materials & Design 32(2), 838–850 (2011) https://doi.org/ 37 10.1016/j.matdes.2010.07.015

Reference 42

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.789825Z

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Observation 8a256ffe-7881-4162-8116-440ab48d8bd1 · outbound

This paper cites The International Journal of Advanced Manufacturing Technology 44(1), 26–37 (2009) https://doi.org/10.1007/s00170-008-1805-x.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach The International Journal of Advanced Manufacturing Technology 44(1), 26–37 (2009) https://doi.org/10.1007/s00170-008-1805-x

Reference 43

Resolution: verified exact; doi, observed 2026-08-12T16:34:35.776206Z

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Observation 7a7d363a-abb4-4d5e-ac37-cbee028b16f1 · outbound

This paper cites Berlin (1968) 38.

Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach Berlin (1968) 38

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:34:36.088332Z

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