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Prof. Zhihao Zhang

Central South University, School of Materials Science and Engineering

Co-Affiliations:University of Science and Technology Beijing

Research Publications & English Decoded Briefs

Showing 2 publications
Transactions of Nonferrous Metals Society of China (中国有色金属学报)2026DOI: 10.1016/S1003-6326(26)67060-4

Effect of Al on Microstructure and Properties of Cu−Be−Ni Alloy Processed by Thermo-Mechanical Treatment

The microstructural evolution and property response of Cu−0.3Be−2.0Ni and Cu−0.3Be−2.0Ni−0.2Al alloys subjected to solution treatment at 950 °C for 30 min, 70% cold rolling, and aging at 450 °C for 60 min were systematically investigated. The baseline Cu−0.3Be−2.0Ni alloy precipitates predominantly the Ni−Be phase with a transformation sequence of γ″→γ′→γ, whereas the Al-modified alloy exhibits co-precipitation of Ni3Al and nanoscale Be−Ni phases. This synergistic precipitation yields a hardness of HV 268, yield strength of 824 MPa, tensile strength of 881 MPa, elongation of 9%, and electrical conductivity of 47% IACS in the Cu−0.3Be−2.0Ni−0.2Al alloy, compared to HV 238, 785 MPa, 840 MPa, 10%, and 50% IACS for the Al-free counterpart. Relative to conventional aging, thermo-mechanical treatment increases hardness by 13% and conductivity by 6.8% in the Al-containing alloy, while the Al-free alloy shows a 6% hardness increase with marginal conductivity improvement. The co-precipitation mechanism effectively compensates for the strength loss typically associated with reduced Be content, demonstrating a viable pathway for low-cost, high-performance Cu−Be alloys.

Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报)2025DOI: 10.1007/s12613-025-3114-x

Digital model for rapid prediction and autonomous control of die forging force for aluminum alloy aviation components

Digital modeling and autonomous control of the die forging process are significant challenges in realizing high-quality intelligent forging of components. Using the die forging of AA2014 aluminum alloy as a case study, a machine-learning-assisted method for digital modeling of the forging force and autonomous control in response to forging parameter disturbances was proposed. First, finite element simulations of the forging processes were conducted under varying friction factors, die temperatures, billet temperatures, and forging velocities, and the sample data, including process parameters and forging force under different forging strokes, were gathered. Prediction models for the forging force were established using the support vector regression algorithm. The prediction error of Ff, that is, the forging force required to fill the die cavity fully, was as low as 4.1%. To further improve the prediction accuracy of the model for the actual Ff, two rounds of iterative forging experiments were conducted using the Bayesian optimization algorithm, and the prediction error of Ff in the forging experiments was reduced from 6.0% to 1.5%. Finally, the prediction model of Ff combined with a genetic algorithm was used to establish an autonomous optimization strategy for the forging velocity at each stage of the forging stroke, when the billet and die temperatures were disturbed, which realized the autonomous control in response to disturbances. In cases of −20 or −40°C reductions in the die and billet temperatures, forging experiments conducted with the autonomous optimization strategy maintained the measured Ff around the target value of 180 t, with the relative error ranging from −1.3% to +3.1%. This work provides a reference for the study of digital modeling and autonomous optimization control of quality factors in the forging process.

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