Jing Yang¹, Zhiming Liu^1,*, Xingchao Li², Zhongyao Wang³, Beitong Li¹, Kaiyang Liu¹, Wang Long⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068353 - 10 November 2025

Abstract Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service. However, due to the complexity of fatigue failure mechanisms, achieving accurate multiaxial fatigue life predictions remains challenging. Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions, making it difficult to maintain reliable life prediction results beyond these constraints. This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life, using Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Fully Connected Neural… More >

Ehsan Akbari¹, Tajbakhsh Navid Chakherlou¹, Hamed Tabrizchi^2,3,*, Amir Mosavi^3,4,5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 305-325, 2025, DOI:10.32604/cmes.2025.068581 - 30 October 2025

Abstract The ability to predict multiaxial fatigue life of Al-Alloy 7075-T6 under complex loading conditions is critical to assessing its durability under complex loading conditions, particularly in aerospace, automotive, and structural applications. This paper presents a physical-informed neural network (PINN) model to predict the fatigue life of Al-Alloy 7075-T6 over a variety of multiaxial stresses. The model integrates the principles of the Geometric Multiaxial Fatigue Life (GMFL) approach, which is a novel fatigue life prediction approach to estimating fatigue life by combining multiple fatigue criteria. The proposed model aims to estimate fatigue damage accumulation by the More >

Lijie Wang¹, Xuguang Dong¹, Yao Lu¹, Xiaoming Du^1,*, Jide Liu²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3787-3803, 2025, DOI:10.32604/cmc.2025.070696 - 23 September 2025

Abstract The available datasets provided by our previous works on creep life for nickel-based single crystal superalloys were analyzed through supervised machine learning to rank features in terms of their importance for determining creep life. We employed six models, namely Back Propagation Neural Network (BPNN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Gaussian Process Regression (GPR), XGBoost, and CatBoost, to predict the creep life. Our investigation showed that the BPNN model with a network structure of “24-7(20)-1” (which consists of 24 input layers, 7 hidden layers, 20 neurons, and 1 output layer) performed better than More >

Pasqualino Corigliano^*, Giulia Palomba

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1935-1955, 2025, DOI:10.32604/cmes.2025.067094 - 31 August 2025

Abstract Experimental tests are essential for evaluating S-N curves and assessing the fatigue life of welded joints. However, in the case of complex geometries, experimental tests often cannot provide the necessary stress-strain data for specific materials and welded joints. Therefore, finite element (FE) analyses are frequently utilized to assess fatigue behavior in complex geometries and address the discontinuities induced by welding processes. In this study, the fatigue properties of titanium welded joints, produced using an innovative laser source and welded without the use of filler materials, were analyzed through numerical methods. Two different FE methods were… More >

Mengke Ding¹, Jun Li^1,2,*, Dongyue Gao^1,*, Guotai Zhou², Borui Wang¹, Zhanjun Wu¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 597-612, 2025, DOI:10.32604/cmc.2025.067907 - 29 August 2025

Abstract Throughout the composite structure’s lifespan, it is subject to a range of environmental factors, including loads, vibrations, and conditions involving heat and humidity. These factors have the potential to compromise the integrity of the structure. The estimation of the fatigue life of composite materials is imperative for ensuring the structural integrity of these materials. In this study, a methodology is proposed for predicting the fatigue life of composites that integrates ultrasonic guided waves and machine learning modeling. The method first screens the ultrasonic guided wave signal features that are significantly affected by fatigue damage. Subsequently,… More >

Ibrahim T. Teke^1,2, Ahmet H. Ertas^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 201-237, 2025, DOI:10.32604/cmes.2025.066644 - 31 July 2025

Abstract Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading. While earlier studies mainly examined material properties and how stress affects lifespan, this review offers the first comprehensive, multiscale comparison of strategies that optimize geometry to improve fatigue performance. This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets, notches, and overall structural layouts. We analyze and combine various methods, including topology and shape optimization, the ability of additive manufacturing to fine-tune internal geometries, and reliability-based More >

Ricardo Dintén^*, Marta Zorrilla

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 239-265, 2025, DOI:10.32604/cmes.2025.065461 - 31 July 2025

Abstract Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events, posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing, which frequently leads to the development of large and complex models. Inspired by the success of Large Language Models (LLMs), transformer-based foundation models have been developed for time series (TSFM). These models have been proven to reconstruct time series in a zero-shot manner, being able to capture different patterns that effectively characterize time series. This paper proposes the use of TSFM to generate… More >

Yuheng Yin, Lin Wang^*

Energy Engineering, Vol.122, No.7, pp. 2845-2864, 2025, DOI:10.32604/ee.2025.065889 - 27 June 2025

Abstract The accurate estimation of lithium battery state of health (SOH) plays an important role in the health management of battery systems. In order to improve the prediction accuracy of SOH, this paper proposes a stochastic configuration network based on a multi-converged black-winged kite search algorithm, called SBKA-CLSCN. Firstly, the indirect health index (HI) of the battery is extracted by combining it with Person correlation coefficients in the battery charging and discharging cycle point data. Secondly, to address the problem that the black-winged kite optimization algorithm (BKA) falls into the local optimum problem and improve the… More >

Yongfeng Tai¹, Xingyu Yan², Xiangyi Geng³, Lin Mu⁴, Mingshun Jiang², Faye Zhang^2,*

Structural Durability & Health Monitoring, Vol.19, No.2, pp. 365-383, 2025, DOI:10.32604/sdhm.2024.053998 - 15 January 2025

Abstract The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee. In engineering scenarios, only a small amount of bearing performance degradation data can be obtained through accelerated life testing. In the absence of lifetime data, the hidden long-term correlation between performance degradation data is challenging to mine effectively, which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method. To address this problem, a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed. Firstly,… More >

Jie Lin^*, Hongchi Shen, Tingting Pei, Yan Wu

Energy Engineering, Vol.122, No.1, pp. 331-347, 2025, DOI:10.32604/ee.2024.055611 - 27 December 2024

Abstract Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unit power generation costs. The service life of these modules directly affects these costs. Over time, the performance of PV modules gradually declines due to internal degradation and external environmental factors. This cumulative degradation impacts the overall reliability of photovoltaic power generation. This study addresses the complex degradation process of PV modules by developing a two-stage Wiener process model. This approach accounts for the distinct phases of degradation resulting from module aging and environmental influences. A power degradation model based on the More > Graphic Abstract