Effects of Fe content and natural aging on the microstructures and mechanical properties of recyclable heat treatment-free Al-7Si-0.3Mg die-cast alloys determined using machine learning

Qian Liu; Xi-Xi Dong; Wen-Jing Han; Qi-Xiu Han; Hai-Lin Yang; Shou-Xun Ji

doi:10.1007/s40436-026-00607-x

Advances in Manufacturing ›› :1 -20. DOI: 10.1007/s40436-026-00607-x

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Effects of Fe content and natural aging on the microstructures and mechanical properties of recyclable heat treatment-free Al-7Si-0.3Mg die-cast alloys determined using machine learning

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¹College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, People’s Republic of China

²State Key Laboratory of Advanced Forming Technology & Equipment, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, People’s Republic of China

³State Key Laboratory of Powder Metallurgy, Central South University, 410083, Changsha, People’s Republic of China

⁴Brunel Centre for Advanced Solidification Technology (BCAST), Brunel University London, UB8 3PH, Uxbridge, United Kingdom

Corresponding author:

^a dongxixi@nuaa.edu.cn

Qian Liu

Qian Liu is currently a master’s degree candidate at Nanjing University of Aeronautics and Astronautics, China. His current research interests include advanced aluminum alloys and artificial intelligence.

Xi-Xi Dong

Xi-Xi Dong is currently a professor at Nanjing University of Aeronautics and Astronautics, China. He obtained the Ph.D. degree from Tsinghua University in 2015. His research interests include advanced light alloys, advanced casting technology, material-design-manufacturing integration, and intelligent manufacturing.

Wen-Jing Han

Wen-Jing Han is currently a bachelor’s degree candidate at Nanjing University of Aeronautics and Astronautics, China. Her current research interests include alloy material design and advanced manufacturing.

Qi-Xiu Han

Qi-Xiu Han is currently pursuing a master’s degree at Nanjing University of Aeronautics and Astronautics, China. Her current research interests include digital twins and cast alloys.

Hai-Lin Yang

Hai-Lin Yang is currently a professor at Central South University, China. He obtained the Ph.D. degree from Central South University in 2010. His research interests include the 3D printing of aluminum alloys and high entropy alloys, refractory metal hard alloys, metal ceramics, and refractory metal-based biomaterials.

Shou-Xun Ji

Shou-Xun Ji is currently a professor at Brunel University London, UK. He obtained the Ph.D. degree from Huazhong University of Science and Technology in 1997. His research area is materials and metallurgy, and his research interests include lightweight alloy materials and structures, advanced casting technology, additive manufacturing, and intelligent manufacturing.

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Abstract

A group of heat-treatment-free Al-7Si-0.3Mg-xFe (x = 0.15, 0.3, 0.45, 0.6 wt%, wt% is mass fraction) die-cast alloys were prepared, and the effects of the Fe content and natural aging (NA) on the microstructures and tensile properties were investigated using machine learning (ML). The intermetallic phases of blocky Mg₂Si and chunky α-Al₁₅(Fe, Mn)₃Si₂ (~0.5–1 μm) were found in the as-cast microstructure, while striped β-Al₁₃(Fe, Mn)₄Si_0.25 with a width of ~2–3 μm appeared until the Fe content exceeded 0.45 wt%. As the Fe content increased from 0.15 wt% to 0.6 wt%, the yield strength (YS) increased from (128.6±2.7) MPa to (132.1±2.5) MPa and the elongation (EL) decreased from (14.35±1.60)% to (10.56±1.32)%. The underlying mechanism was attributed to the increase in the number density of α-Fe from (1.03±0.09)×10¹⁰/m² to (1.11±0.08)×10¹¹/m², along with the increase in that of β-Fe from zero to (1.73±0.16)×10⁹/m². The relationships among the Fe content and the as-cast YS (MPa), ultimate tensile strength (UTS) (MPa), and EL (%) were quantitatively determined (YS = 129.03−4.54x+16.53x², UTS= −22.76x²+21.64x+262.94, and EL=−8.2x+15.59). The investigated alloys showed a YS enhancement of 5.8–9.8 MPa after NA for 30 d, which was attributed to the appearance of β″ nanoscale precipitates. The adaptive boosting (AdaBoost) ML model performed the best among the four investigated ML models, with a high R² of 0.92 and low mean absolute error of 0.53 when predicting the YS of the Al-7Si-0.3Mg-xFe die-cast alloys. The quantitative relationships among the Fe content, NA time, and YS/UTS/EL values were determined using the AdaBoost model (YS = 137.74−29.07exp(−x/4.35t(d)/3.91), UTS=265.1+7.73x+0.22t−5.95x², and EL=15.52−4.89/((1+exp((0.16−x)/0.16))(1+exp((46.55−t)/(−6.34))))). A high-performance and recyclable Al-7Si-0.3Mg-0.5Fe alloy was predicted using AdaBoost, with YS, UTS, and EL values of (137.8±1.8) MPa, (272.6±1.8) MPa, and (12.03±1.18)% after NA for 30 d. The results of this study provide an effective method to intelligently develop high-performance and recyclable heat-treatment-free die-cast alloys.

Keywords

Aluminum alloys / Microstructure / Natural aging (NA) / Mechanical properties / Recyclability / Machine learning (ML)

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Qian Liu, Xi-Xi Dong, Wen-Jing Han, Qi-Xiu Han, Hai-Lin Yang, Shou-Xun Ji. Effects of Fe content and natural aging on the microstructures and mechanical properties of recyclable heat treatment-free Al-7Si-0.3Mg die-cast alloys determined using machine learning. Advances in Manufacturing 1-20 DOI:10.1007/s40436-026-00607-x

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