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Paper ID:IJSREM66514

A Comprehensive Review of Magnesium Alloy AZ61: Microstructure, Processing, Properties, and the Emergence of AI/ML-Assisted Optimization

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM66514

Volume:

10 |

8 |

Aug 2026

Pages:

1-10

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM66514

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DAT

Dr. Amit Tiwari

Mechanical Engineering

Suresh Gyan Vihar University, Jaipur

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HV

Himanshu Vasnani

Department of Mechanical Engineering,

Suresh Gyan Vihar University, Jaipur

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Abstract

Magnesium alloy AZ61 (nominally Mg-6Al-1Zn, with minor Mn) occupies an important middle ground in the Mg-Al-Zn (AZ) family, offering a balance between the high castability of AZ91 and the superior ductility of AZ31. Its low density (~1.80 g/cm3), high specific strength, good damping capacity, and excellent machinability make it attractive for automotive, aerospace, electronics, and biomedical implant applications. This review consolidates work from the last decade on the alloy's microstructure - dominated by an alpha-Mg matrix and the intermetallic ß-Mg17Al12 phase - and links microstructural evolution to mechanical, corrosion, and fatigue behavior. Processing routes including casting, hot extrusion, equal-channel angular pressing (ECAP), friction stir processing (FSP), and rolling are compared, showing that severe plastic deformation (SPD) can raise yield strength from ~95 MPa (as-cast) to ~245 MPa while improving elongation through grain refinement. We synthesize the findings of roughly thirty research groups, summarize their key results and limitations, and highlight the rapid growth of AI/ML approaches - artificial neural networks, random forests, support vector regression, and genetic-algorithm optimization - for predicting and optimizing AZ61 properties. Persistent limitations, particularly poor corrosion resistance, room-temperature formability, and elevated-temperature creep, are discussed alongside surface-engineering remedies. The review closes with future directions emphasizing data-driven alloy and process design.

KEYWORDS

AZ61 magnesium alloy; ß-Mg17Al12; severe plastic deformation; corrosion; machine learning; optimization.

Cite This Article

Dr. Amit Tiwari, Himanshu Vasnani, "A Comprehensive Review of Magnesium Alloy AZ61: Microstructure, Processing, Properties, and the Emergence of AI/ML-Assisted Optimization," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 8, pp. 1-10, Aug 2026. https://doi.org/10.55041/IJSREM66514

Full Article/Conclusion

1.      AZ61 is a balanced wrought Mg-Al-Zn alloy whose properties are dictated by the alpha-Mg matrix and the ß-Mg17Al12 intermetallic; controlling beta amount, morphology, and distribution is the central design problem. 2.      SPD (ECAP, FSP) and extrusion refine grains and modify texture, raising YS from ~95 MPa (as-cast) toward ~245 MPa with elongation beyond 20% in optimized ECAP states. 3.      Corrosion is the principal limitation, driven by micro-galvanic alpha/beta coupling and impurities; PEO/MAO and polymer coatings reduce corrosion rate by roughly an order of magnitude. 4.      AI/ML and optimization methods (ANN, RF, SVR, GA, Taguchi) now reliably predict flow stress, recrystallization, mechanical properties, and corrosion, accelerating optimization despite data and interpretability bottlenecks. 5.      Future work should prioritize physics-informed, explainable ML on standardized open datasets, controlled-degradation biomedical designs, durable self-healing coatings, and thermally stable microstructures.

References

1.    Guo, W., Shi, J., Lü, S., Xiang, K., Yan, Z., Wu, Z., ... & Wu, S. (2026). Achieving synergistic strength-ductility optimization in SiCₚ/AZ61 composites by a novel continuous squeeze casting-extrusion process. Materials Characterization, 115972. 2.    Huang, S. J., Subramani, M., & Chiang, C. C. (2021). Effect of hybrid reinforcement on microstructure and mechanical properties of AZ61 magnesium alloy processed by stir casting method. Composites communications, 25, 100772. 3.    Tiwari, A., Sharma, A., & Nayak, S. (2026). Multi-objective Computational Optimization of AZ61 Magnesium Alloy Rolling Parameters Using Monte Carlo Simulation Framework. Journal of Materials Engineering and Performance, 1-19. 4.    Kumar, Y., Tiwari, A., Vasnani, H., & Kumar, N. (2018). Investigation of mechanical behavior of Al 6063 & SiC composite materials. International Journal of Advanced Technology and Engineering Exploration, 5(47), 376-384. 5.    Tiwari, A., Vasnani, H., Jain, R., Swarnkar, H., Tanwar, H., & Srivastava, S. (2024). Fabrication of low-cost metal polishing machine for preparation of microstructure samples of lightweight alloy and composite material. Global Journal of Engineering and Technology Advances, 19(3), 112-123. 6.    Tiwari, A., Jain, R., Swarnkar, H., Bansal, P., Mathur, M., & Vasnani, H. (2025). Effect of graphene-based paraffin composite on performance enhancement of IoT-integrated box-type solar cooker. Environmental Science and Pollution Research, 1-24. 7.    Tiwari, A., Bansal, P., Vasnani, H., & Pandel, N. (2025, January). Non Dominated Sorting Genetic Algorithm (NSGA-II) Modelling for AZ 31 Magnesium Alloy Composite. In 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI) (pp. 268-272). IEEE. 8.    Tiwari, A., Soni, P. K., & Nayak, S. (2025). GA Optimization for Performance and Emission Characteristics of SI Engine via Ethanol-Gasoline Blend. In Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications (pp. 317-348). IGI Global Scientific Publishing. 9.    Tiwari, A., Bansal, P., Pandel, N., Vasnani, H., Amrousse, R., & Azat, S. (2025). AI-Driven Design of AZ61 Magnesium Alloy for Enhanced Performance in Industrial Automation. In Advancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses (pp. 153-172). IGI Global Scientific Publishing. 10. Tiwari, A., & Kumar, A. (2025). Synergistic Effects of Sc and Ca on the Performance of Modified AZ61 Mg Alloy in Defense Application. In Innovative Materials for Next-Generation Defense Applications: Cost, Performance, and Mass Production (pp. 315-352). IGI Global Scientific Publishing.

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Publication Date

8/4/2026

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