JOM最新文献 - Book学术

Study on High-Performance Gear Fatigue Life Prediction Method Based on Deep Learning Theories 基于深度学习理论的高性能齿轮疲劳寿命预测方法研究

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-16 DOI: 10.1007/s11837-024-06952-1

Xingbin Chen, Yanxia Xu, Xilong Zhang, Yibing Yin

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引用次数: 0

From Discussions to Decisions: An Overview of TMS Events at MS&T24

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-09 DOI: 10.1007/s11837-024-07046-8

Megan Enright, Kelly Zappas

引用次数: 0

Navigate Your TMS Membership with New Video Orientation Series

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07042-y

Kelly Zappas

引用次数: 0

TMS Members Gain Valuable Experience at 2024 Emerging Leaders Alliance Program TMS 会员在 2024 新兴领袖联盟计划中获得宝贵经验

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07043-x

Kaitin Calva

引用次数: 0

JOM Technical Topics

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07041-z

引用次数: 0

TMS Meeting Headlines

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07048-6

引用次数: 0

Melting Before Our Eyes: A Materials Art Mystery

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07045-9

Kaitlin Calva

引用次数: 0

New Editors Announced for Metallurgical and Materials Transactions Journals

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07047-7

Kelly Zappas

引用次数: 0

JOM Talks with Irina Iachina of Biomimica

IF 2.1 4区材料科学

JOM Pub Date : 2024-12-04 DOI: 10.1007/s11837-024-07044-w

引用次数: 0

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

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

{"title":"Modeling and Prediction Method for Young’s Moduli of Ti Alloys Based on Residual Muti-layer Perceptron","authors":"Hua Yan, Qiang Li, Bin Yang, Yang Yang, Ying Wang, Hao Zhang","doi":"10.1007/s11837-024-06942-3","DOIUrl":"10.1007/s11837-024-06942-3","url":null,"abstract":"<div><p>Accurate Young’s modulus models of <i>β</i>-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.</p></div>","PeriodicalId":605,"journal":{"name":"JOM","volume":"77 1","pages":"76 - 90"},"PeriodicalIF":2.1,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0