Effective residual stress prediction validated with neutron diffraction method for metal large-scale additive manufacturing

Nycz, Andrzej; Lee, Yousub; Noakes, Mark; Ankit, Deo; Masuo, Christopher; Simunovic, Srdjan; Bunn, Jeff; Love, Lonnie; Oancea, Victor; Payzant, Andrew; Fancher, Chris M.

doi:10.1016/j.matdes.2021.109751

Title: Effective residual stress prediction validated with neutron diffraction method for metal large-scale additive manufacturing

Journal Article · 30 June 2021 · Materials & Design

DOI: https://doi.org/10.1016/j.matdes.2021.109751 · OSTI ID:1779528

Nycz, Andrzej; Lee, Yousub; Noakes, Mark; Ankit, Deo; Masuo, Christopher; Simunovic, Srdjan; Bunn, Jeff; Love, Lonnie; Oancea, Victor;

; Fancher, Chris M.

Not Available

View Journal Article

Cite

Export

Save

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1779528

Journal Information:: Materials & Design, Journal Name: Materials & Design Journal Issue: C Vol. 205; ISSN 0264-1275

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (22)

A new finite element model for welding heat sources Goldak, John; Chakravarti, Aditya; Bibby, Malcolm Metallurgical Transactions B, Vol. 15, Issue 2 https://doi.org/10.1007/BF02667333	journal	June 1984
Improving prediction accuracy of thermal analysis for weld-based additive manufacturing by calibrating input parameters using IR imaging Bai, Xingwang; Zhang, Haiou; Wang, Guilan The International Journal of Advanced Manufacturing Technology, Vol. 69, Issue 5-8 https://doi.org/10.1007/s00170-013-5102-y	journal	June 2013
Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel Tapia, Gustavo; Khairallah, Saad; Matthews, Manyalibo The International Journal of Advanced Manufacturing Technology, Vol. 94, Issue 9-12 https://doi.org/10.1007/s00170-017-1045-z	journal	September 2017
Residual strain evolution in steel samples: tension versus torsion Bunn, J. R.; Penumadu, D.; Hubbard, C. R. Applied Physics A, Vol. 99, Issue 3 https://doi.org/10.1007/s00339-010-5616-1	journal	March 2010
A 3D Finite Difference Thermal Model Tailored for Additive Manufacturing Stockman, Tom; Schneider, Judith A.; Walker, Bryant JOM, Vol. 71, Issue 3 https://doi.org/10.1007/s11837-019-03338-6	journal	January 2019
Thermo-mechanical modeling of thermal stress in metal additive manufacturing considering elastoplastic hardening Mirkoohi, Elham; Sievers, Daniel E.; Garmestani, Hamid CIRP Journal of Manufacturing Science and Technology, Vol. 28 https://doi.org/10.1016/j.cirpj.2020.01.002	journal	January 2020
Numerical simulation of welding distortion in large structures Deng, Dean; Murakawa, Hidekazu; Liang, Wei Computer Methods in Applied Mechanics and Engineering, Vol. 196, Issue 45-48 https://doi.org/10.1016/j.cma.2007.05.023	journal	September 2007
Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives Qi, Xinbo; Chen, Guofeng; Li, Yong Engineering, Vol. 5, Issue 4 https://doi.org/10.1016/j.eng.2019.04.012	journal	August 2019
Residual stress and texture control in Ti-6Al-4V wire + arc additively manufactured intersections by stress relief and rolling Hönnige, J. R.; Colegrove, P. A.; Ahmad, B. Materials & Design, Vol. 150 https://doi.org/10.1016/j.matdes.2018.03.065	journal	July 2018
Advances in additive manufacturing process simulation: Residual stresses and distortion predictions in complex metallic components Song, Xu; Feih, Stefanie; Zhai, Wei Materials & Design, Vol. 193 https://doi.org/10.1016/j.matdes.2020.108779	journal	August 2020
High-fidelity modelling of thermal stress for additive manufacturing by linking thermal-fluid and mechanical models Chen, Fan; Yan, Wentao Materials & Design, Vol. 196 https://doi.org/10.1016/j.matdes.2020.109185	journal	November 2020
Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data Francis, Jack; Bian, Linkan Manufacturing Letters, Vol. 20 https://doi.org/10.1016/j.mfglet.2019.02.001	journal	April 2019
Towards an integrated experimental and computational framework for large-scale metal additive manufacturing Hu, Xiaohua; Nycz, Andrzej; Lee, Yousub Materials Science and Engineering: A, Vol. 761 https://doi.org/10.1016/j.msea.2019.138057	journal	July 2019
Residual Stress in Metal Additive Manufacturing Li, C.; Liu, Z. Y.; Fang, X. Y. Procedia CIRP, Vol. 71 https://doi.org/10.1016/j.procir.2018.05.039	journal	January 2018
A practical path planning methodology for wire and arc additive manufacturing of thin-walled structures Ding, Donghong; Pan, Zengxi; Cuiuri, Dominic Robotics and Computer-Integrated Manufacturing, Vol. 34 https://doi.org/10.1016/j.rcim.2015.01.003	journal	August 2015
Current capabilities of the residual stress diffractometer at the high flux isotope reactor Cornwell, P.; Bunn, J.; Fancher, C. M. Review of Scientific Instruments, Vol. 89, Issue 9 https://doi.org/10.1063/1.5037593	journal	September 2018
Thermomechanical Modeling of Additive Manufacturing Large Parts Denlinger, Erik R.; Irwin, Jeff; Michaleris, Pan Journal of Manufacturing Science and Engineering, Vol. 136, Issue 6 https://doi.org/10.1115/1.4028669	journal	October 2014
Part Build Orientation Optimization and Neural Network-Based Geometry Compensation for Additive Manufacturing Process Chowdhury, Sushmit; Mhapsekar, Kunal; Anand, Sam Journal of Manufacturing Science and Engineering, Vol. 140, Issue 3 https://doi.org/10.1115/1.4038293	journal	December 2017
Introduction to the Characterization of Residual Stress by Neutron Diffraction Hutchings, M. T.; Withers, P. J.; Holden, T. M. https://doi.org/10.1201/9780203402818	book	January 2005
Correlation of Microstructure and Mechanical Properties of Metal Big Area Additive Manufacturing Shassere, Benjamin; Nycz, Andrzej; Noakes, Mark Applied Sciences, Vol. 9, Issue 4 https://doi.org/10.3390/app9040787	journal	February 2019
Effect of Interlayer Cooling Time, Constraint and Tool Path Strategy on Deformation of Large Components Made by Laser Metal Deposition with Wire Lee, Yousub; Bandari, Yashwanth; Nandwana, Peeyush Applied Sciences, Vol. 9, Issue 23 https://doi.org/10.3390/app9235115	journal	November 2019
Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM) Rodrigues, Tiago A.; Duarte, V.; Miranda, R. M. Materials, Vol. 12, Issue 7 https://doi.org/10.3390/ma12071121	journal	April 2019

Similar Records

Prediction of residual strain/stress validated with neutron diffraction method for wire-feed hybrid additive/subtractive manufacturing

Journal Article · 2023 · Additive Manufacturing · OSTI ID:2229934

Lee, Yousub; Feldhausen, Thomas; Fancher, Chris M.; +4 more

Residual stress mapping in Inconel 625 fabricated through additive manufacturing: Method for neutron diffraction measurements to validate thermomechanical model predictions

Journal Article · 2016 · Materials & Design · OSTI ID:1399026

Wang, Zhuqing; Denlinger, Erik; Michaleris, Panagiotis; +3 more

Neutron diffraction measurements of residual stress in additively manufactured stainless steel

Journal Article · 2016 · Materials Science and Engineering. A, Structural Materials: Properties, Microstructure and Processing · OSTI ID:1398030

Brown, D. W.; Bernardin, J. D.; Carpenter, J. S.; +3 more

Related Subjects

36 MATERIALS SCIENCE
distortion
finite element analysis
large-scale additive manufacturing
neutron diffraction
residual stress

Title: Effective residual stress prediction validated with neutron diffraction method for metal large-scale additive manufacturing

Citation Formats

References (22)

Similar Records

Related Subjects