Key Takeaways & Executive Findings
- •• A machine-learning-assisted digital model predicts die forging force with high accuracy (error as low as 4.1% in simulation, 1.5% after Bayesian optimization). • Autonomous control strategy using genetic algorithm adjusts forging velocity to maintain target forging force despite temperature disturbances, keeping relative error within −1.3% to +3.1%. • The approach integrates finite element simulation, support vector regression, Bayesian optimization, and genetic algorithms for intelligent forging process control. • This work provides a practical framework for real-time quality control in aluminum alloy aviation component forging, enhancing consistency and reducing defects.
Abstract
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.
1. Introduction
In recent years, aluminum alloy forging processes for high-end manufacturing have significantly evolved in scale, complexity, and precision [1–3], which has led to an increased focus on forming quality [4–7]. The forging conditions are often unexpectedly altered during the forging process. For example, variations in the billet and die temperatures owing to natural cooling, lubricant application, fluctuations in furnace temperature, and other factors can lead to fluctuations in the forging force. These fluctuations result in forging process instability and affect the forming quality, causing inconsistencies in the microstructure and mechanical properties of the components [8–12]. Consequently, the development of a rapid prediction model that reflects the impact of parameter variations and enables autonomous optimization control of the forging process is crucial for producing high-quality forgings.
Technological advancements in computing and artificial intelligence have propelled material processing toward automation and intelligence [13–14]. Li et al. [15] proposed an intelligent optimization strategy integrating machine learning and numerical simulation technologies that significantly reduced the initial design space of the forging die parameters and enabled rapid die size optimization. Azari et al. [16] used a variety of machine learning algorithms to predict the radial forging force, which offered an effective approach for designing the initial billet temperature, die entry angle, feed velocity, and cross-sectional reduction rate. Kampen et al. [17] utilized a genetic algorithm to optimize the geometry of a connecting rod preform, resulting in a flash rate of 7%, which is significantly less than that achieved without this optimization (14.9%). Chen and Lin [18] proposed an online optimization method for the hot-deformation process parameters of a GH4169 superalloy based on an artificial neural network, reporting that the microstructure of the optimized alloy exhibited increased uniformity and reduced grain size. These studies are excellent examples of research on digital modeling and intelligent control in forging processes.
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Hao Hu, Fan Zhao, Daoxiang Wu, Zhengan Wang, Zhilei Wang, Zhihao Zhang, Weidong Li, Jianxin Xie (2025). Digital model for rapid prediction and autonomous control of die forging force for aluminum alloy aviation components. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-025-3114-x
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Frequently Asked Questions
What is the main objective of this study?
The study aims to develop a digital model for rapid prediction and autonomous control of die forging force for aluminum alloy aviation components, specifically using AA2014 alloy, to ensure high-quality intelligent forging despite parameter disturbances.
How was the forging force prediction model developed?
The model was developed using finite element simulations under varying process parameters, followed by support vector regression (SVR) to predict forging force. Bayesian optimization was then used to refine the model with experimental data, reducing prediction error from 6.0% to 1.5%.
What is the autonomous control strategy proposed?
The strategy combines the prediction model with a genetic algorithm to optimize forging velocity at each stage of the forging stroke in response to temperature disturbances, maintaining the forging force near the target value (180 t) with relative errors between −1.3% and +3.1%.
What are the key benefits of this approach?
The approach enables real-time adjustment of forging parameters to compensate for disturbances, improving process stability and product consistency, and reducing the need for trial-and-error in industrial forging.
What is the significance of this work for the industry?
It provides a practical framework for intelligent control in forging processes, which can be adapted to other materials and processes, enhancing efficiency and quality in high-end manufacturing.
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