Modeling and Prediction Method for Young’s Moduli of Ti Alloys Based on Residual Muti-layer Perceptron

IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM Pub Date : 2024-11-20 DOI:10.1007/s11837-024-06942-3
Hua Yan, Qiang Li, Bin Yang, Yang Yang, Ying Wang, Hao Zhang
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引用次数: 0

Abstract

Accurate Young’s modulus models of β-type Ti alloys can provide a convenient approach to developing Ti alloy, especially non-toxic and biocompatible medical materials. Data-driven approaches can significantly reduce the difficulty of modeling Young’s modulus of Ti alloy and build reliable models by relying on the large amount of available historical data. Therefore, a deep learning model using multi-layer perceptron with residual connection, namely Res-MLP, is designed to establish the Young’s modulus model of Ti alloy according to its element content or composition. Benchmark models are selected for performance comparison, of which performances metrics, including MAE, MAPE, RMSE, and MARNE, are 9.83, 15.05%, 14.86, and 10.57%, respectively. Therefore, the Res-MLP has predictive ability. Compared to SVR, XGBoost, RF, BPNN, CNN, and MLP models, Res-MLP achieves better prediction performance and precision. Moreover, the bootstrapping algorithm is used to expand the sample size. Through a comparative analysis of the predictive performance of Res-MLP before and after dataset expansion, it is demonstrated that data augmentation methods can effectively enhance predictive capabilities. Consequently, the model proposed in this study can provide an effective and efficient data mining tool developing medical Ti alloy materials.

Abstract Image

基于残差多层感知器的钛合金杨氏模量建模与预测方法
准确的β型钛合金杨氏模量模型为开发钛合金,特别是无毒和生物相容性医用材料提供了一种方便的方法。数据驱动方法可以显著降低钛合金杨氏模量建模的难度,并依靠大量可用的历史数据建立可靠的模型。因此,设计了一种基于残差连接的多层感知器深度学习模型Res-MLP,根据钛合金的元素含量或成分建立其杨氏模量模型。选择基准模型进行性能比较,其中MAE、MAPE、RMSE、MARNE的性能指标分别为9.83、15.05%、14.86、10.57%。因此,Res-MLP具有预测能力。与SVR、XGBoost、RF、BPNN、CNN和MLP模型相比,Res-MLP模型具有更好的预测性能和精度。此外,采用自举算法扩大样本容量。通过对数据扩充前后Res-MLP预测性能的对比分析,表明数据扩充方法可以有效增强预测能力。因此,本研究提出的模型可以为医用钛合金材料的开发提供一个有效的数据挖掘工具。
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来源期刊
JOM
JOM 工程技术-材料科学:综合
CiteScore
4.50
自引率
3.80%
发文量
540
审稿时长
2.8 months
期刊介绍: JOM is a technical journal devoted to exploring the many aspects of materials science and engineering. JOM reports scholarly work that explores the state-of-the-art processing, fabrication, design, and application of metals, ceramics, plastics, composites, and other materials. In pursuing this goal, JOM strives to balance the interests of the laboratory and the marketplace by reporting academic, industrial, and government-sponsored work from around the world.
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