Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approaches. However, correlations between process parameters, thermal profiles, and resulting microstructure in AFSD still need to be better understood. This hinders process optimization for properties. This work employs a framework combining supervised machine learning (SML) and physics-informed neural networks (PINNs) to predict peak temperature distribution in AFSD from process parameters. Eight regression algorithms were implemented for SML modeling, while four PINNs leveraged governing equations for transport, wave propagation, heat transfer, and quantum mechanics. Across multiple statistical measures, ensemble techniques like gradient boosting proved superior for SML, with the lowest MSE of 165.78. The integrated ML approach was also applied to classify deposition quality from process factors, with logistic regression delivering robust accuracy. By fusing data-driven learning and fundamental physics, this dual methodology provides comprehensive insights into tailoring microstructure through thermal management in AFSD. The work demonstrates the power of bridging statistical and physics-based modeling for elucidating AM process-property relationships.

Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Department of Industrial Engineering College of Engineering King Saud University Riyadh Saudi Arabia

Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB Technical University of Ostrava Ostrava Czech Republic

Department of Mechanical Engineering Gazi University Faculty of Engineering Maltepe Ankara Turkey

Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan

Department of Mechanical Engineering School of Engineering and Applied Sciences Bennett University Noida Uttar Pradesh India

School of Industrial and Information Engineering Politecnico Di Milano Milan Italy

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Maleki E, Bagherifard S, Bandini M, Guagliano M. Surface post-treatments for metal additive manufacturing: Progress, challenges, and opportunities. Additive Manufacturing. 2021;37:101619. doi: 10.1016/j.addma.2020.101619 DOI

Xu Z, La Mendola I, Razavi SMJ, Bagherifard S. Additive manufactured triply periodic minimal surface lattice structures with modulated hybrid topology. Engineering Structures. 2023;289:116249.

Maleki E, Bagherifard S, Heydari Astaraee A, Sgarbazzini S, Bandini M, Guagliano M. Application of gradient severe shot peening as a novel mechanical surface treatment on fatigue behavior of additively manufactured AlSi10Mg. Materials Science and Engineering: A. 2023;881:145397. doi: 10.1016/j.msea.2023.145397 DOI

Tamburrino F, Graziosi S, Bordegoni M. The design process of additively manufactured mesoscale lattice structures: a review. Journal of Computing and Information Science in Engineering. 2018;18(4):040801.

Bregoli C, Buccino F, Picca F, Bagherifard S, Biffi CA, Tuissi A, et al.. Additive manufacturing of AISI 316L specimens with distributed inner bone-type cavities: processability and characterization. IOP Conference Series: Materials Science and Engineering. 2023;1275(1):012001.

Agrawal P, Haridas RS, Yadav S, Thapliyal S, Gaddam S, Verma R, et al.. Processing-structure-property correlation in additive friction stir deposited Ti-6Al-4V alloy from recycled metal chips. Additive Manufacturing. 2021;47:102259.

Agrawal P, Haridas RS, Mishra RS. Deformation-based additive manufacturing of a metastable high entropy alloy via Additive friction stir deposition. Additive Manufacturing. 2022;60:103282. doi: 10.1016/j.addma.2022.103282 DOI

Martin LP, Luccitti A, Walluk M. Repair of aluminum 6061 plates by additive friction stir deposition. The International Journal of Advanced Manufacturing Technology. 2022:1-15.

Khodabakhshi F, Gerlich AP. Potentials and strategies of solid-state additive friction-stir manufacturing technology: A critical review. Journal of Manufacturing Processes. 2018;36:77–92.

Yu HZ, Hahn GD. Potential and challenges for large-scale near-net-shaping of 7xxx aerospace grade aluminum via additive friction stir deposition. Materials Letters: X. 2023;19:100217. doi: 10.1016/j.mlblux.2023.100217 DOI

Wu B, Peng Y, Tang H, Meng C, Zhong Y, Zhang F, et al.. Improving grain structure and dispersoid distribution of nanodiamond reinforced AA6061 matrix composite coatings via high-speed additive friction stir deposition. Journal of Materials Processing Technology. 2023;317:117983. doi: 10.1016/j.jmatprotec.2023.117983 DOI

Li Y, Yang B, Zhang M, Wang H, Gong W, Lai R, et al. The corrosion behavior and mechanical properties of 5083 Al-Mg alloy manufactured by additive friction stir deposition. Corrosion Science. 2023;213:110972.

Mukhopadhyay A, Saha P, Singh PK, Verma M. Development and analysis of a powder bed friction stir (PBFS) additive manufacturing process for aluminum alloys: A study on friction-stirring pitch (ω/v) and print location. Additive Manufacturing. 2023;72:103618.

El-Sayed Seleman MM, Ataya S, Ahmed MMZ, Hassan AMM, Latief FH, Hajlaoui K, et al.. The Additive Manufacturing of Aluminum Matrix Nano Al2O3 Composites Produced via Friction Stir Deposition Using Different Initial Material Conditions. Materials (Basel). 2022;15(8):2926. doi: 10.3390/ma15082926 PubMed DOI PMC

Yoder JK, Hahn GD, Zhao N, Brennan RE, Cho K, Hang ZY. Additive friction stir deposition-enabled upcycling of automotive cast aluminum chips. Additive Manufacturing Letters. 2023;4:100108.

Robinson TW, Williams MB, Rao HM, Kinser RP, Allison PG, Jordon JB. Microstructural and mechanical properties of a solid-state additive manufactured magnesium alloy. Journal of Manufacturing Science and Engineering. 2022;144(6):061013.

Choudhury S, Acharya U, Roy J, Roy BS. Recent progress in solid-state additive manufacturing technique: Friction stir additive manufacturing. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 2022;237(2):467–91. doi: 10.1177/09544089221107755 DOI

Ralls AM, Menezes PL. Understanding the tribal-corrosion mechanisms of friction stir processed steel deposited by high-pressure deposition additive manufacturing process. The International Journal of Advanced Manufacturing Technology. 2023;128(1):823–43.

Liu L, Xu W, Zhao Y, Lin Z, Liu Z, Dong Y, et al.. Tailoring the microstructure and mechanical properties of wire and arc additive manufactured Al-Mg alloy via interlayer friction stir processing. Journal of Materials Research and Technology. 2023.

Sharma S, Krishna KM, Radhakrishnan M, Pantawane MV, Patil SM, Joshi SS, et al.. A pseudo thermo-mechanical model linking process parameters to microstructural evolution in multilayer additive friction stir deposition of magnesium alloy. Materials & Design. 2022;224:111412.

Joshi SS, Patil SM, Mazumder S, Sharma S, Riley DA, Dowden S, et al.. Additive friction stir deposition of AZ31B magnesium alloy. Journal of Magnesium and Alloys. 2022;10(9):2404–20. doi: 10.1016/j.jma.2022.03.011 DOI

Gotawala N, Hang ZY. Material flow path and extreme thermomechanical processing history during additive friction stir deposition. Journal of Manufacturing Processes. 2023;101:114–27.

Chaudhary B, Jain NK, Murugesan J. Experimental investigation and parametric optimization of friction stir powder additive manufacturing process for aerospace-grade Al alloy. The International Journal of Advanced Manufacturing Technology. 2022;123(1–2):603–25.

Chaudhary B, Jain NK, Murugesan J. Development of friction stir powder deposition process for repairing aerospace-grade aluminum alloys. CIRP Journal of Manufacturing Science and Technology. 2022;38:252–67.