From Mechanics to Machine Learning in Additive Manufacturing: A Review of Deformation, Fatigue, and Fracture

Murat Demiral; Murat Otkur

doi:10.3390/technologies14040218

journal article Open Access Apr 09, 2026

From Mechanics to Machine Learning in Additive Manufacturing: A Review of Deformation, Fatigue, and Fracture

Murat Demiral

Murat Otkur

Technologies Vol. 14 No. 4 pp. 218 · MDPI AG

View at Publisher Save 10.3390/technologies14040218

Abstract

Additive manufacturing (AM) enables a level of design flexibility that is difficult to achieve with conventional techniques, yet it inherently yields materials marked by significant variability, anisotropy, and sensitivity to defects that challenge classical mechanics-of-materials assumptions. Process-driven microstructural heterogeneity, stochastic defect populations, and residual stresses strongly influence deformation, fatigue, and fracture behavior, often outweighing nominal material properties and constraining the predictive capability of traditional constitutive and fracture mechanics models. Machine learning (ML) has emerged as a powerful means of handling the complexity of AM data; however, many current approaches depend on black-box models that lack physical transparency, extrapolate poorly, and treat uncertainty inadequately. This review contends that ML should augment—rather than replace—mechanics-based modeling, and that dependable prediction of AM material behavior requires mechanics-informed ML frameworks. We critically analyze the central mechanics challenges in AM and evaluate established modeling strategies alongside emerging ML methods relevant to deformation, damage, fatigue, and fracture. Particular emphasis is given to physics-informed and hybrid ML approaches that explicitly incorporate anisotropy, defect sensitivity, residual stress effects, and uncertainty quantification within learning architectures. Recent progress in ML-assisted constitutive modeling, fatigue and fracture prediction, and digital twin development is synthesized, and the implications for qualification, certification, and structural deployment of AM components are discussed.

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References

180

[1]

Demiral, M., Saracyakupoglu, T., Sahin, B., and Köklü, U. (2025). Minimizing delamination in CFRP laminates: Experimental and numerical insights into drilling and punching effects. Polymers, 17. 10.3390/polym17223056

[2]

Additive Manufacturing-A Review

Gulnaaz Rasiya, Abhinav Shukla, Karan Saran

Materials Today: Proceedings 2020 10.1016/j.matpr.2021.05.181

[3]

Kumar "Methods and materials for additive manufacturing: A critical review on advancements and challenges" Thin-Walled Struct. (2021) 10.1016/j.tws.2020.107228

[4]

Vafadar, A., Guzzomi, F., Rassau, A., and Hayward, K. (2021). Advances in metal additive manufacturing: A review of common processes, industrial applications, and current challenges. Appl. Sci., 11. 10.3390/app11031213

[5]

(2026, January 13). Stratasys, Metal Parts on Demand. Available online: https://www.stratasysdirect.com/materials/metals#view%20selectio.

[6]

Yao "Establishing the intrinsic connection between microstructure, 3D defects, and the corrosion behavior of selective laser melted WE43 alloys" Rare Met. (2025) 10.1007/s12598-025-03463-z

[7]

Howard, L., Parker, G.D., and Yu, X.-Y. (2024). Progress and Challenges of Additive Manufacturing of Tungsten and Alloys as Plasma-Facing Materials. Materials, 17. 10.3390/ma17092104

[8]

Talignani "A review on additive manufacturing of refractory tungsten and tungsten alloys" Addit. Manuf. (2022)

[9]

Enneti "Effect of process parameters on the Selective Laser Melting (SLM) of tungsten" Int. J. Refract. Met. Hard Mater. (2018) 10.1016/j.ijrmhm.2017.11.035

[10]

Zhang "Influence of Particle Size on Laser Absorption and Scanning Track Formation Mechanisms of Pure Tungsten Powder During Selective Laser Melting" Engineering (2019) 10.1016/j.eng.2019.07.003

[11]

Raj "Tungsten carbide-cobalt based hardmetals via additive manufacturing: Insights into processing–structure–property relationships" Ceram. Int. (2026) 10.1016/j.ceramint.2025.12.284

[12]

Padmakumar "Additive Manufacturing of Tungsten Carbide Hardmetal Parts by Selective Laser Melting (SLM), Selective Laser Sintering (SLS) and Binder Jet 3D Printing (BJ3DP) Techniques" Lasers Manuf. Mater. Process. (2020) 10.1007/s40516-020-00124-0

[13]

Tan "Selective laser melting of high-performance pure tungsten: Parameter design, densification behavior and mechanical properties" Sci. Technol. Adv. Mater. (2018) 10.1080/14686996.2018.1455154

[14]

Kaserer "Fully dense and crack free molybdenum manufactured by Selective Laser Melting through alloying with carbon" Int. J. Refract. Met. Hard Mater. (2019) 10.1016/j.ijrmhm.2019.105000

[15]

Braun "Molybdenum and tungsten manufactured by selective laser melting: Analysis of defect structure and solidification mechanisms" Int. J. Refract. Met. Hard Mater. (2019) 10.1016/j.ijrmhm.2019.104999

[16]

Wang "Microstructures, Thermal and Mechanical Properties of Pure Tungsten—A Comparison Between Selective Laser Melting and Hot Rolling" Chin. J. Mech. Eng. (2022) 10.1186/s10033-022-00712-5

[17]

Song "Differences in microstructure and properties between selective laser melting and traditional manufacturing for fabrication of metal parts: A review" Front. Mech. Eng. (2015) 10.1007/s11465-015-0341-2

[18]

Yasa, E., Deckers, J., Kruth, J.-P., Rombouts, M., and Luyten, J. (2009, January 6–10). Experimental investigation of charpy impact tests on metallic SLM parts. Proceedings of the 4th International Conference on Advanced Research in Virtual and Physical Prototyping, Leiria, Portugal.

[19]

Dong "Effect of atmosphere on the microstructure and properties of additively manufactured tungsten" Mater. Sci. Technol. (2020) 10.1080/02670836.2020.1852680

[20]

Schlick "Additive manufacturing of pure tungsten by means of selective laser beam melting with substrate preheating temperatures up to 1000 °C" Nucl. Mater. Energy (2019) 10.1016/j.nme.2019.02.034

[21]

Additive manufacture of low melting point metal porous materials: Capabilities, potential applications and challenges

Jian-Ye Gao, Sen Chen, Tian-Ying Liu et al.

Materials Today 2021 10.1016/j.mattod.2021.03.019

[22]

Wang, L. (2024). 3D printing of liquid metal. Handbook of Liquid Metals, Springer. 10.1007/978-981-97-1614-2_25

[23]

Jin "Unlocking the potential of low-melting-point alloys integrated extrusion additive manufacturing: Insights into mechanical behavior, energy absorption, and electrical conductivity" Prog. Addit. Manuf. (2025) 10.1007/s40964-024-00780-0

[24]

Liu "Review on Fatigue of Additive Manufactured Metallic Alloys: Microstructure, Performance, Enhancement, and Assessment Methods" Adv. Mater. (2024) 10.1002/adma.202306570

[25]

Fatigue performance of metal additive manufacturing: a comprehensive overview

Hamidreza Javidrad, Bahattin Koc, Hakan Bayraktar et al.

Virtual and Physical Prototyping 2024 10.1080/17452759.2024.2302556

[26]

Quinsat "Effects of additive manufacturing processes on part defects and properties: A classification review" Int. J. Interact. Des. Manuf. (2022) 10.1007/s12008-022-00839-8

[27]

Gandhi, R., Maccioni, L., and Concli, F. (2022). Significant Advancements in Numerical Simulation of Fatigue Behavior in Metal Additive Manufacturing-Review. Appl. Sci., 12. 10.3390/app122111132

[28]

Afazov "Defect-based fatigue model for additive manufacturing" Prog. Addit. Manuf. (2023) 10.1007/s40964-022-00376-6

[29]

Wang "Machine learning in additive manufacturing: State-of-the-art and perspectives" Addit. Manuf. (2020)

[30]

Meng "Machine Learning in Additive Manufacturing: A Review" J. Miner. Met. Mater. Soc. (2020) 10.1007/s11837-020-04155-y

[31]

Kwak "Multiscale mechanical characterization of 601 nickel-based superalloy fabricated using wire-arc additive manufacturing" Mater. Sci. Eng. A (2022) 10.1016/j.msea.2022.142734

[32]

Haldar "On Fabrication of Inconel 718 Slab by Wire Arc Additive Manufacturing: Study of Built Microstructure and Mechanical Properties" Arab. J. Sci. Eng. (2023) 10.1007/s13369-023-08095-y

[33]

Xiong "Microstructure-sensitive fracture in additive manufactured Ni-based superalloys: A coupled crystal plasticity and phase-field modeling" Eng. Fract. Mech. (2025) 10.1016/j.engfracmech.2025.111683

[34]

Evans "Understanding the impact of texture on the micromechanical anisotropy of laser powder bed fused Inconel 718" J. Mater. Sci. (2022) 10.1007/s10853-022-07499-9

[35]

Bagherzadeh "Directional-adaptive approach in machining of additively manufactured Inconel 718" CIRP Ann. (2025) 10.1016/j.cirp.2025.04.023

[36]

Hong "On influence of microstructural anisotropy of additive manufactured structures upon machining dynamics: An example of milling of Ti6Al4V thin-walled parts" J. Mech. Work. Technol. (2024)

[37]

Tevet, O., Svetlizky, D., Harel, D., Barkay, Z., Geva, D., and Eliaz, N. (2022). Measurement of the Anisotropic Dynamic Elastic Constants of Additive Manufactured and Wrought Ti6Al4V Alloys. Materials, 15. 10.3390/ma15020638

[38]

Song "Deciphering phase stress partition and its correlation to mechanical anisotropy of laser powder bed fusion AlSi10Mg" Addit. Manuf. (2023)

[39]

Pitrmuc, Z., Šimota, J., Beránek, L., Mikeš, P., Andronov, V., Sommer, J., and Holešovský, F. (2022). Mechanical and Microstructural Anisotropy of Laser Powder Bed Fusion 316L Stainless Steel. Materials, 15. 10.3390/ma15020551

[40]

Demiral "Deformation characteristics in micro-cutting of single crystal copper: Effect of cutting tool geometry and friction" J. Mech. Sci. Technol. (2022) 10.1007/s12206-022-0327-z

[41]

Demiral "Numerical modelling of size effects in micro-cutting of f.c.c. single crystal: Influence of strain gradients" J. Manuf. Process. (2022) 10.1016/j.jmapro.2021.12.036

[42]

Chen "Defect inspection technologies for additive manufacturing" Int. J. Extrem. Manuf. (2021) 10.1088/2631-7990/abe0d0

[43]

Bellini "Additive manufacturing processes for metals and effects of defects on mechanical strength: A review" Procedia Struct. Integr. (2021) 10.1016/j.prostr.2021.10.057

[44]

Haribaskar "Defects in Metal Additive Manufacturing: Formation, Process Parameters, Postprocessing, Challenges, Economic Aspects, and Future Research Directions" 3D Print. Addit. Manuf. (2024) 10.1089/3dp.2022.0344

[45]

Uddin "Insights into Fatigue Damage in Additive Manufacturing through Process-Structure-Property Interactions" J. Fail. Anal. Prev. (2025) 10.1007/s11668-025-02305-5

[46]

Stern "Non-destructive characterization of process-induced defects and their effect on the fatigue behavior of austenitic steel 316L made by laser-powder bed fusion" Prog. Addit. Manuf. (2020) 10.1007/s40964-019-00105-6

[47]

Akgun "Fatigue of laser powder-bed fusion additive manufactured Ti-6Al-4V in presence of process-induced porosity defects" Eng. Fract. Mech. (2022) 10.1016/j.engfracmech.2021.108140

[48]

Zhan "Assessment of the effect of the process-induced porosity defects on the fatigue properties of wire arc additive manufactured Al–Si–Mg alloy" J. Mater. Res. Technol. (2025) 10.1016/j.jmrt.2025.01.080

[49]

Arbeiter "Damage tolerance-based methodology for fatigue lifetime estimation of a structural component produced by material extrusion-based additive manufacturing" Addit. Manuf. (2020)

[50]

Cao "Compression experiment and numerical evaluation on mechanical responses of the lattice structures with stochastic geometric defects originated from additive-manufacturing" Compos. Part B Eng. (2020) 10.1016/j.compositesb.2020.108030

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Published: Apr 09, 2026
Vol/Issue: 14(4)
Pages: 218
License: View

Authors

M

Murat Demiral

College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

M

Murat Otkur

College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

Cite This Article

Murat Demiral, Murat Otkur (2026). From Mechanics to Machine Learning in Additive Manufacturing: A Review of Deformation, Fatigue, and Fracture. Technologies, 14(4), 218. https://doi.org/10.3390/technologies14040218

From Mechanics to Machine Learning in Additive Manufacturing: A Review of Deformation, Fatigue, and Fracture

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