A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts | Paper Citation Record

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 1

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This paper cites Baumann, Nilesh Kumar, and Rajiv S.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Baumann, Nilesh Kumar, and Rajiv S

Reference 2

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This paper cites Residual stress evaluation in api 5l x65 girth welded pipes joined by friction welding and gas tungsten arc welding.Journal of Materials Research and Technology, 8(1):988–95, 2019.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Residual stress evaluation in api 5l x65 girth welded pipes joined by friction welding and gas tungsten arc welding.Journal of Materials Research and Technology, 8(1):988–95, 2019

Reference 3

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Reference 4

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 5

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This paper cites Chantikul, B.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Chantikul, B

Reference 6

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 7

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Observation 424077c2-64ca-4d62-a8ba-46685f7f1444 · outbound

This paper cites Angular distortion and through-thickness residual stress distribution in the friction-stir processed 6061-t6 aluminum alloy.Materials Science and Engineering A, 437(1):64–69, 2006.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Angular distortion and through-thickness residual stress distribution in the friction-stir processed 6061-t6 aluminum alloy.Materials Science and Engineering A, 437(1):64–69, 2006

Reference 8

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Reference 9

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This paper cites McDowell.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts McDowell

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.960510Z

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Observation 81c54d39-23ca-45ac-8012-6b7eb4b14096 · outbound

This paper cites Material flow visualization during friction stir welding using high-speed x-ray imaging.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Material flow visualization during friction stir welding using high-speed x-ray imaging

Reference 11

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 12

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 13

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This paper cites Song and R.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Song and R

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.926553Z

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This paper cites Coupling Smoothed Particle Hydrodynamics With Finite Element Method to Simulate Residual Stresses From Friction Stir Processing.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Coupling Smoothed Particle Hydrodynamics With Finite Element Method to Simulate Residual Stresses From Friction Stir Processing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.918300Z

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This paper cites Escobar, Hrishikesh Das, Shivakant Shukla, Benjamin J.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Escobar, Hrishikesh Das, Shivakant Shukla, Benjamin J

Reference 16

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 17

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 18

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This paper cites The measurement of residual stresses by the incremental hole drilling technique.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts The measurement of residual stresses by the incremental hole drilling technique

Reference 19

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 20

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Observation 96bb5d67-464a-419c-94d5-958467f1ef04 · outbound

This paper cites Micromechanics of defects in solids, 1987.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Micromechanics of defects in solids, 1987

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.865735Z

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 22

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 23

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This paper cites Korsunsky.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Korsunsky

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.836925Z

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Observation 6e956ae2-63dc-4476-bb27-2d2e17e03118 · outbound

This paper cites Finite element analysis and machine learning guided design of carbon fiber organosheet-based battery enclosures for crashworthiness.Applied Composite Materials, 0123456789, 2024.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Finite element analysis and machine learning guided design of carbon fiber organosheet-based battery enclosures for crashworthiness.Applied Composite Materials, 0123456789, 2024

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Observation 3e77d117-4729-4c4f-be0a-dee6e2612870 · outbound

This paper cites Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance

Reference 26

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Observation c45c2878-bf34-4eb0-969b-cc18ebb2428a · outbound

This paper cites Prediction of composite microstructure stress- strain curves using convolutional neural networks.Materials & Design, 189:108509, 2020.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Prediction of composite microstructure stress- strain curves using convolutional neural networks.Materials & Design, 189:108509, 2020

Reference 27

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This paper cites Stress field prediction in fiber-reinforced composite materials using a deep learning approach.Composites Part B: Engineering, 238:109879, 2022.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Stress field prediction in fiber-reinforced composite materials using a deep learning approach.Composites Part B: Engineering, 238:109879, 2022

Reference 28

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Observation e70ed9ef-4228-4964-9197-4a4d319c8c7c · outbound

This paper cites Data-driven methods for stress field predictions in random heterogeneous materials.Engineering Applications of Artificial Intelligence, 123:106267, 2023.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Data-driven methods for stress field predictions in random heterogeneous materials.Engineering Applications of Artificial Intelligence, 123:106267, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.801616Z

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Observation 70d0d969-3d19-4c08-a7d2-8deb4404b13b · outbound

This paper cites Improved deep learning method for accurate flow field reconstruction from sparse data.Ocean Engineering, 280:114902, 2023.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Improved deep learning method for accurate flow field reconstruction from sparse data.Ocean Engineering, 280:114902, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.792558Z

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Observation 51bf3e3d-694b-43f8-b66d-12fe7176e6f1 · outbound

This paper cites Robust flow reconstruction from limited measurements via sparse representation.Physical Review Fluids, 4(10):103907, 2019.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Robust flow reconstruction from limited measurements via sparse representation.Physical Review Fluids, 4(10):103907, 2019

Reference 31

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Observation 7020fed0-e253-4e64-93fa-72ff675f3d3e · outbound

This paper cites A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network.Acta Mechanica Sinica, 39(3):322302, 2023.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts A practical approach to flow field reconstruction with sparse or incomplete data through physics informed neural network.Acta Mechanica Sinica, 39(3):322302, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.774261Z

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This paper cites Fully convolutional networks for semantic segmentation.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Fully convolutional networks for semantic segmentation

Reference 33

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This paper cites U-net: Convolutional networks for biomedical image segmentation.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts U-net: Convolutional networks for biomedical image segmentation

Reference 34

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This paper cites High-resolution image synthesis with latent diffusion models.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts High-resolution image synthesis with latent diffusion models

Reference 35

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This paper cites Schmidt and J.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Schmidt and J

Reference 36

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 37

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This paper cites Abaqus 6.14 CAE User Guide.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Abaqus 6.14 CAE User Guide

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.727352Z

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This paper cites On the utility of the thermal-pseudo mechanical model ’ s residual stress prediction capability for the development of friction stir processing on the utility of the thermal.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts On the utility of the thermal-pseudo mechanical model ’ s residual stress prediction capability for the development of friction stir processing on the utility of the thermal

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.718389Z

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 40

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Observation 801e4d39-6332-4a86-b40f-27aead39ba46 · outbound

This paper cites Denseunet: densely connected unet for electron microscopy image segmentation.IET Image Processing, 14(12):2682–2689, 2020.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Denseunet: densely connected unet for electron microscopy image segmentation.IET Image Processing, 14(12):2682–2689, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.700081Z

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This paper cites Visual geometry group-unet: deep learning ultrasonic image reconstruction for curved parts.The Journal of the Acoustical Society of America, 149(5):2997–3009, 2021.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Visual geometry group-unet: deep learning ultrasonic image reconstruction for curved parts.The Journal of the Acoustical Society of America, 149(5):2997–3009, 2021

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This paper cites Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J

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Observation 3d45541c-133e-4b95-823a-e5a526bd408c · outbound

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A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 44

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Observation c646daa2-4521-4d9b-b7b4-295875039740 · outbound

This paper cites A deep learning approach for the velocity field prediction in a scramjet isolator.Physics of Fluids, 33(2), 2021.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts A deep learning approach for the velocity field prediction in a scramjet isolator.Physics of Fluids, 33(2), 2021

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Observation 34cd4fe5-24a9-4517-9ce0-90bfffc91fbc · outbound

This paper cites Topology optimization using super- resolution image reconstruction methods.Advances in Engineering Software, 177:103413, 2023.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Topology optimization using super- resolution image reconstruction methods.Advances in Engineering Software, 177:103413, 2023

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Observation 0faff574-64aa-4e2c-aff4-3af43f8b1887 · outbound

This paper cites Image quality metrics: Psnr vs.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Image quality metrics: Psnr vs

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.647490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5cf46e05-2415-4799-b09c-a5a05b6c24ad · outbound

This paper cites Multiscale structural similarity for image quality assessment.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Multiscale structural similarity for image quality assessment

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.638229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fee39c0a-3482-4d6c-ab79-f92ec8d991ff · outbound

This paper cites an unresolved cited work.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:51.458118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd9894fc-70e4-4b33-9a9d-6e78a631cf55 · outbound

This paper cites Ma- chine learning–accelerated computational fluid dynamics.Proceedings of the National Academy of Sciences, 118(21):e2101784118, 2021.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Ma- chine learning–accelerated computational fluid dynamics.Proceedings of the National Academy of Sciences, 118(21):e2101784118, 2021

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:51.461852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d19952b7-e797-4693-a013-7125fdc929fa · outbound

This paper cites Grant, and Saumyadeep Jana.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Grant, and Saumyadeep Jana

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.618448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6aa5617f-9289-4549-98b5-ee8f3d0933ea · outbound

This paper cites Residual Stress Measurement by ESPI Hole-Drilling.Procedia CIRP, 45:203–206, 2016.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Residual Stress Measurement by ESPI Hole-Drilling.Procedia CIRP, 45:203–206, 2016

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.609738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89653573-a718-4be1-9d90-292e2de44506 · outbound

This paper cites Steinzig and T.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Steinzig and T

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.600966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66d07658-5afb-4b31-bb25-a410229718a5 · outbound

This paper cites an unresolved cited work.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:25:51.592451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa7893cf-9132-4544-8dca-a362fb289bd7 · outbound

This paper cites an unresolved cited work.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:25:51.583870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bfe0fad5-0c92-43f3-956a-28319c92cd1f · outbound

This paper cites Steinzig and E.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Steinzig and E

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.574696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2cacafb-25a4-4d5f-a7e5-bb9d7e65c55c · outbound

This paper cites Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.565112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67c0ef72-8353-4467-afa8-20dbc2c581d3 · outbound

This paper cites an unresolved cited work.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:25:51.556143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 750e0043-59e7-47c5-b132-e2a2420a4103 · outbound

This paper cites Reinforcing the Exit Hole from Friction Stir Welding and Processing.SSRN Electronic Journal, 26(August):101611, 2022.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Reinforcing the Exit Hole from Friction Stir Welding and Processing.SSRN Electronic Journal, 26(August):101611, 2022

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.547013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f2344573-6ea7-4efe-8264-6601c8ff4e6c · outbound

This paper cites Martínez.

A Machine Learning Approach to Generate Residual Stress Distributions using Sparse Characterization Data in Friction-Stir Processed Parts Martínez

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:25:51.537398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Outbound references

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