Abstract
Simulation is a very useful tool in the design of the part and process conditions of high-pressure die casting (HPDC), due to the intrinsic complexity of this manufacturing process. Usually, physics-based models solved by finite element or finite volume methods are used, but their main drawback is the long calculation time. In order to apply optimization strategies in the design process or to implement online predictive systems, faster models are required. One solution is the use of surrogate models, also called metamodels or grey-box models. The novelty of the work presented here lies in the development of several metamodels for the HPDC process. These metamodels are based on a gradient boosting regressor technique and derived from a physics-based finite element model. The results show that the developed metamodels are able to predict with high accuracy the secondary dendrite arm spacing (SDAS) of the cast parts and, with good accuracy, the misrun risk and the shrinkage level. Results obtained in the predictions of microporosity and macroporosity, eutectic percentage, and grain density were less accurate. The metamodels were very fast (less than 1 s); therefore, they can be used for optimization activities or be integrated into online prediction systems for the HPDC industry. The case study corresponds to several parts of aluminum cast alloys, used in the automotive industry, manufactured by high-pressure die casting in a multicavity mold.
Highlights
A large number of scientific and engineering fields study complex real-world phenomena or solve challenging design problems with simulation techniques [1,2,3,4]
The response variables are the values that the metamodel must predict. They correspond to several key performance indicators (KPIs) representative of the part quality, and they were selected taking into account their interest for the metal casting industry, their influence on the part performance, and the data availability
The performance of the model was evaluated by comparing the predicted values with the reference values using the R2 score and the normalization of the error (NMAE), defined in the coming lines
Summary
A large number of scientific and engineering fields study complex real-world phenomena or solve challenging design problems with simulation techniques [1,2,3,4]. In many cases, the computational cost of these simulations makes their use impossible for real-time predictions and limits their application in optimization tasks. The use of machine learning techniques such as neural networks or ensemble methods has become a useful alternative to avoid these limitations. The metamodeling approach has been used in many fields [5,6,7], but for the particular case of HPDC process, the number of works is reduced. The work of Fiorese et al in [8,9] was focused on the use of new predictive variables derived from plunger movement and the prediction of the ultimate strength of the cast parts. Yao et al [11] based their metamodel on a Gaussian process regression and predicted the temperature at the end of filling
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