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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 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-16 DOI: 10.1007/s11837-024-06952-1
Xingbin Chen, Yanxia Xu, Xilong Zhang, Yibing Yin

This paper studies fatigue application scenarios for high-performance gears and other mechanical components. It addresses the limitations of internal encapsulation detection and challenges of long-cycle tests. The paper proposes an intelligent prediction method for fatigue features, utilizing visual detection and accelerated degradation life. It integrates conventional test benches and environmental reliability accelerated test conditions, conducts in-depth research on fatigue life estimation algorithms, and explores the feasibility of employing deep learning algorithms and failure prediction models for fatigue life prediction. The paper also establishes an algorithmic system architecture that integrates and processes information from multiple systems and sensors, including gear fatigue performance driving and fatigue monitoring. This approach enables the rapid identification of early micro-motion fatigue characteristics, online autonomous detection, and intelligent failure estimation by integrating information from various systems and sensors. It can accurately predict fatigue degradation and provide a basis for adopting a rational anti-fatigue optimization design.

本文研究了高性能齿轮和其他机械部件的疲劳应用场景。它解决了内部封装检测的局限性和长周期测试的挑战。论文提出了一种利用视觉检测和加速退化寿命的疲劳特征智能预测方法。它整合了传统试验台和环境可靠性加速试验条件，对疲劳寿命估算算法进行了深入研究，并探索了采用深度学习算法和失效预测模型进行疲劳寿命预测的可行性。论文还建立了一个算法系统架构，可集成和处理来自多个系统和传感器的信息，包括齿轮疲劳性能驾驶和疲劳监测。这种方法通过整合来自不同系统和传感器的信息，实现了早期微动疲劳特征的快速识别、在线自主检测和智能故障预估。它能准确预测疲劳退化，为采用合理的抗疲劳优化设计提供依据。
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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 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-09 DOI: 10.1007/s11837-024-07046-8
Megan Enright, Kelly Zappas
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引用次数: 0
Navigate Your TMS Membership with New Video Orientation Series
IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07042-y
Kelly Zappas
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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 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07043-x
Kaitin Calva
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引用次数: 0
JOM Technical Topics
IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07041-z
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引用次数: 0
TMS Meeting Headlines
IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07048-6
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引用次数: 0
Melting Before Our Eyes: A Materials Art Mystery
IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07045-9
Kaitlin Calva
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引用次数: 0
New Editors Announced for Metallurgical and Materials Transactions Journals
IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-05 DOI: 10.1007/s11837-024-07047-7
Kelly Zappas
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引用次数: 0
JOM Talks with Irina Iachina of Biomimica
IF 2.1 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
JOM
Pub Date : 2024-12-04 DOI: 10.1007/s11837-024-07044-w
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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 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

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.

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引用次数: 0
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