Influence of Different Dimensionality Reduction Strategies on Prediction of Mechanical Properties of Castings

TANG Binhao; MENG Zhenghua; MA Qiang; LYU Shulin

doi:10.3969/j.issn.1674-6457.2026.05.017

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (5) : 179-187. DOI: 10.3969/j.issn.1674-6457.2026.05.017

Iron and Steel Forming

Influence of Different Dimensionality Reduction Strategies on Prediction of Mechanical Properties of Castings

TANG Binhao^1,2,3,*, MENG Zhenghua^1,3, MA Qiang², LYU Shulin⁴

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Abstract

The work aims to accurately predict the mechanical properties of cast steel using machine learning models, avoid interference from redundant features, and explore effective dimensionality reduction strategies for tensile strength, yield strength, and hardness data of steel castings, thereby achieving precise predictions and improving prediction efficiency. Based on a “material composition and heat treatment process-mechanical properties” dataset of cast steel, three dimensionality reduction strategies, namely, Spearman correlation analysis, Adaboost importance analysis, and genetic algorithm-based feature selection, combined with five machine learning algorithms of Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), BP Neural Network, and Stacking models were systematically compared in terms of model performance. The results showed that the maximum coefficients of determination for yield strength, tensile strength, and hardness reached 0.9, 0.94, and 0.902, respectively, under the orthogonal experiments combining dimensionality reduction strategies and machine learning models. The optimal combination for predicting yield strength was the Spearman feature selection method paired with the Random Forest (RF) model. For tensile strength, the best prediction accuracy was achieved using the Spearman feature selection method combined with the Stacking model. In the case of hardness, the genetic algorithm-based feature selection method coupled with the RF model yielded the highest prediction accuracy. Additionally, RF-based feature importance analysis revealed that elements such as C, Mn, and Mo, along with heat treatment processes, significantly influenced the mechanical properties of cast steel. This study systematically evaluates the performance of various combinations of dimensionality reduction strategies and machine learning algorithms. The findings demonstrate that appropriate dimensionality reduction strategies, when paired with suitable algorithmic models, can effectively enhance prediction accuracy. This provides a crucial theoretical foundation for the design of cast steel materials and offers practical guidance for advancing the intelligent development of casting technology.

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cast steel / mechanical performance prediction / machine learning / dimensionality reduction strategy / feature importance

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TANG Binhao, MENG Zhenghua, MA Qiang, LYU Shulin. Influence of Different Dimensionality Reduction Strategies on Prediction of Mechanical Properties of Castings[J]. Journal of Netshape Forming Engineering. 2026, 18(5): 179-187 https://doi.org/10.3969/j.issn.1674-6457.2026.05.017

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