Jul 2026· SAE technical paper series· 0 citations· 11 references
Abstract
This study proposes a data-driven surrogate modeling framework for predicting
solidification time and mold thermal stress during low-pressure die casting
(LPDC) of aluminum alloy wheels. The methodology employed an optimal Latin
hypercube design (OLHD) to sample key parameters including cooling channel
geometry and process conditions. A sequential simulation methodology combining
ProCAST and Abaqus was implemented to generate a comprehensive dataset of
solidification times and thermal stress distributions. Based on this dataset,
surrogate models were developed using Support Vector Regression, Kriging, and
Polynomial Response Surface Methodology, with their hyperparameters
automatically tuned through Bayesian Optimization (BO). The optimized models
were rigorously evaluated using four statistical metrics: Coefficient of
Determination (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root
Mean Squared Error (RMSE). The evaluation results show that the BO–SVR model
demonstrated superior prediction accuracy for both output responses and
exhibited exceptional nonlinear fitting capability. This work establishes an
effective modeling approach for simultaneous quality and efficiency optimization
in wheel manufacturing.
In low-pressure die casting (LPDC), there is a complicated relationship between the solidification behavior of the casting and the thermal stress of the mold. Conventional studies consider these two aspects separately, complicating global co-optimization. To address this, the present paper puts forward a methodology fo...
Fu-Hao Fan, Yunlang Zhan, Zhen-Fei Zhan et al.· Proceedings of the Instituti...· 0 citations
In steel production plants, finite element (FE) simulations are widely used to develop plastic deformation processes such as rolling and forging. However, these simulations are large-scale and highly nonlinear, and therefore require long computation times, which makes systematic optimization of process conditions diffi...
Hiroto Goto, P. Eberhard· International Journal of Mat...· 0 citations
A novel, data-driven framework for predicting and optimizing the mechanical performance of 3D-printed polylactic acid (PLA) composites reinforced with date pit (DP) particles under controlled annealing conditions is presented, enabling simultaneous property prediction and design optimization from a minimal experimental...
A combined Physics-Informed Neural Network (PINN) model is recommended to predict HVOF thermal sprayed carbon-based composite coatings residual stress distributions and achieves a better accuracy and much lower cost of computation than traditional ANN and FEA-driven surrogate models.
Ankit Tyagi, A. Dadhich, Sachin Sirohi et al.· Scientific Reports· 0 citations
Accurate prediction of the shear strength of reinforced concrete (RC) beams remains a challenging problem due to the complex nonlinear interactions among material properties, reinforcement characteristics, and beam geometry. This study presents a data-driven artificial neural network (ANN) framework for predicting th...
F. Khalid, Milad Razbin, S. Fareed et al.· Scientific Reports· 0 citations
Overall, this study establishes an effective data-driven optimization strategy for quality-oriented design and decision support in intelligent family-mold processes that effectively balance dimensional accuracy and stress reduction while satisfying material usage requirements.
Quoc-Nguyen Banh, Phat-Dat Truong, T. Nguyen et al.· Measurement and control (Lon...· 2 citations
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