| 基于机器学习的低弹性模量钛合金建模和预测的研究 |
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| 引用本文： | 潘登,李谦,李强.基于机器学习的低弹性模量钛合金建模和预测的研究[J].上海理工大学学报,2023,44(5):1-8. |
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| 作者姓名： | 潘登 李谦 李强 |
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| 作者单位： | 上海大学 材料基因组工程研究院, 上海 200444;上海理工大学 机械工程学院, 上海 200093 |
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| 基金项目： | 上海高性能医疗器械材料工程技术研究中心（20DZ2255500） |
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| 摘 要： | 针对钛合金弹性模量快速预测的需要，采用合金设计公式对原始合金数据进行转换，利用转换所得的Mo当量、d-电子结合次数和d-电子结合能作为数据集；采用多层感知器、随机森林网络和卷积神经网络三种机器学习方法，基于数据驱动方式搭建钛合金成分与弹性模量的关系模型。结果表明，相比随机森林网络模型和卷积神经网络模型，多层感知器模型具有更优的预测性能和预测精度。此外，多层感知器模型的预测能力符合预期，其相关指数评分达到0.66，均方根误差为7.54 GPa；说明多层感知器适用于医用钛合金的数据挖掘和研发。
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| 关 键 词： | 钛合金 弹性模量 机器学习 |
| 收稿时间： | 2023/10/12 0:00:00 |
Research on the modeling and predicting low elastic modulus titanium alloys based on machine learning |
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| PAN Deng,LI Qian,LI Qiang.Research on the modeling and predicting low elastic modulus titanium alloys based on machine learning[J].Journal of University of Shanghai For Science and Technology,2023,44(5):1-8. |
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| Authors: | PAN Deng LI Qian LI Qiang |
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| Affiliation: | Materials Genome Institute, Shanghai University, Shanghai 200444, China;School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China |
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| Abstract: | Aiming at the need for rapid prediction of elastic modulus of titanium alloy, the original alloy data were converted by the alloy design formulas, and the molybdenum equivalent, d-electron binding order and d-electron binding energy obtained by the conversions were used as the original dataset. Three machine learning methods, namely multilayer perceptron, random forest network, and convolutional neural network, were used to build the relationship model between titanium alloy compositions and the elastic moduli based on data-driven mode. The results show that compared with random forest network model and convolutional neural network model, multi-layer perceptron model has better prediction performance and accuracy. In addition, the prediction ability of the multi-layer perceptron model meets the expectation, its correlation index score reaches 0.66, and the root mean square error is 7.54 GPa, suggesting that multi-layer perceptron is suitable for the data mining and development of biomedical titanium alloys. |
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| Keywords: | titanium alloy elastic modulus machine learning |
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