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probabilistic machine learning for preventing fatigue failures in Additively Manufactured SS316L

ENGINEERING FAILURE ANALYSIS 2025年 168卷

作者： Centola, Alessio Ciampaglia, Alberto Paolino, Davide Salvatore Tridello, Andrea Politecn Torino Dept Mech & Aerosp Engn I-10129 Turin Italy

This study presents a probabilistic machine learning approach to predict and improve the fatigue performance of additively manufactured SS316L components. By analyzing key manufacturing parameters as process settings, thermal treatments and surface treatments, the developed models provide statistical estimations of fatigue strength, that provides valuable insights to extend fatigue life. Trained on an experimental database of fatigue tests, the Bayesian Neural Network (BNN) is employed to separate model uncertainty, related to data limitations, from the uncertainty inherent in the fatigue phenomenon. This approach robustly predicts probabilistic StressLife (PSN) curves, offering valuable insights into the impact of manufacturing parameters on fatigue resistance, allowing to further postpone fatigue failures. The results demonstrate increased robustness and trustworthiness compared to other deterministic machine learning models, making this method suitable for critical applications where failure prevention is crucial.

关键词： Fatigue probabilistic machine learning machine learning Process parameters Design Additive manufacturing SS316L Failure prevention

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probabilistic machine learning-Based Frequency Normalization Method for Bridge Damage Detection Considering Environmental Variations

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2025年第0期

作者： Pei, Xue-Yang Zhang, He-Tang Huang, Hai-Bin Liang, Dong Yancheng Inst Technol Sch Civil Engn Yancheng 224051 Peoples R China Hebei Univ Technol Sch Civil & Transportat Engn Tianjin 300401 Peoples R China Tiancheng Zhichuang Tianjin Technol Co Ltd Tianjin 300101 Peoples R China

The bridge natural frequency changes caused by structural damages are often masked by the variations of environmental factors (especially the temperature), thus the ability to detect early damage is usually weakened. In order to tolerate the environmental effects, this paper proposes to normalize the natural frequency data based on probabilistic machine learning for the early damage detection of bridge structures. First, the probabilistic nonlinear relationships between multi-order natural frequencies in the intact state are modeled by the Gaussian process regression or the relevance vector machine. Second, the normalized frequency residuals for each order of frequency are individually calculated through their conditional means and variances estimated from the built probabilistic nonlinear relationship models. Third, the lifting residual vectors corresponding to the normalized frequency residuals are constructed according to the statistical localization approach. And finally, the Mahalanobis squared distance of the residual vector is defined as the damage indicator, afterwards the corresponding damage threshold is determined by the kernel density estimation technique. The long-term natural frequency data from a numerical simulation and an actual bridge are used to verify the effectiveness of the proposed method. The results demonstrate that the normalized frequency residuals are scarcely affected by the environmental variations, while remaining high sensitivity to the structural damages;compared to the normalized frequency residual, the damage indicator defined from the lifting residual vector is more suitable for detecting the early damage of bridge structures.

关键词： Bridge early damage detection varying environmental condition frequency normalization probabilistic machine learning statistical localization approach

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probabilistic machine learning pipeline using topological descriptors for real-time state estimation of high-rate dynamic systems

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 227卷

作者： Chua, Yang Kang Coble, Daniel Razmarashooli, Arman Paul, Steve Martinez, Daniel A. Salazar Hu, Chao Downey, Austin R. J. Laflamme, Simon Univ Connecticut Sch Mech Aerosp & Mfg Engn Storrs CT 06269 USA Univ South Carolina Dept Mech Engn Columbia SC USA Iowa State Univ Dept Civil Construct & Environm Engn Ames IA USA Univ South Carolina Dept Civil & Environm Engn Columbia SC USA Iowa State Univ Dept Elect & Comp Engn Ames IA USA Duke Univ Thomas Lord Dept Mech Engn & Mat Sci Durham NC USA

High-rate systems are structures that undergo rapid changes, exhibiting dynamics that evolve over short durations, often less than 100 ms. In this study, we propose a probabilistic machine learning pipeline for estimating the state of a high-rate system. Our approach begins with the extraction of features using topological data analysis (TDA) that capture the underlying structure of datasets. We examine the design of probabilistic models for structural state estimation, emphasizing the importance of prediction intervals. Our method validation involves two datasets: a toy example of linear chirp signals and an experimental dataset from the Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research (DROPBEAR) testbed. We use metrics such as mean absolute error (MAE) and time response assurance criterion (TRAC), along with uncertainty metrics such as negative log-likelihood (NLL), calibration curves, and expected calibration error (ECE), to evaluate model performance. The results indicate that the RNN-NNE model achieves the lowest MAE of 5.705 mm, the highest TRAC, and the lowest ECE of 7.335%, highlighting its superior predictive accuracy and robustness in handling uncertainty.

关键词： Structural health monitoring High-rate systems Nonlinear time series Topological data analysis probabilistic machine learning Uncertainty quantification

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probabilistic machine learning to improve generalisation of data-driven turbulence modelling

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COMPUTERS & FLUIDS 2024年 284卷

作者： Ho, Joel Pepper, Nick Dodwell, Tim Univ Exeter Exeter England Alan Turing Inst London England

A probabilistic machine learning model is introduced to augment the k - omega SST turbulence model in order to improve the modelling of separated flows and the generalisability of learnt corrections. Increasingly, machine learning methods have been used to leverage experimental and high-fidelity simulation data, improving the accuracy of the Reynolds Averaged Navier-Stokes (RANS) turbulence models widely used in industry. A significant challenge for such methods is their ability to generalise to unseen geometries and flow conditions. Furthermore, heterogeneous datasets containing a mix of experimental and simulation data must be efficiently handled. In this work, field inversion and an ensemble of Gaussian Process Emulators (GPEs) is employed to address both of these challenges. The ensemble model is applied to a range of benchmark test cases, demonstrating improved turbulence modelling for cases involving separated flows with adverse pressure gradients, where RANS simulations are understood to be unreliable. Perhaps more significantly, the simulation reverted to the uncorrected model in regions of the flow exhibiting physics outside of the training data.

关键词： Computational fluid dynamics probabilistic machine learning Field inversion Turbulence modelling Gaussian Process Emulators Deep ensembles

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probabilistic machine learning framework for chemical source term integration with Gaussian Processes: H2/air auto-ignition case

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 81卷 47-55页

作者： Ustun, Cihat Emre Paykani, Amin Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England

The integration of chemistry poses a major bottleneck in numerical combustion modelling, as a significant amount of simulation time is consumed in the direct integration (DI) of differential equations into thermochemistry modelling. In this work, a probabilistic machine learning (ML) framework, using Gaussian processes (GPs), is developed as a chemical source term integrator to replace DI for the prediction of a H-2/air autoignition case. In this context, two algorithms, namely Gaussian Process Regression (GPR) and Gaussian Process Autoregressive Regression (GPAR), are investigated. The training dataset is generated using zero-dimensional (0D) isobaric H-2/air auto-ignition simulations in Cantera. To address the scalability issue of the GPs, the variational inducing points method is used. This method leverages a subset of the original data for training, allowing sparse approximations. The performances of the GPR and GPAR are compared to a standard artificial neural network (ANN) model. A priori comparison with direct integration shows that both GPR (R-test(2) = 0.997) and GPAR (R-test(2) = 0.998) outperform the ANNs (R-test(2) = 0.988) by capturing latent dynamics of the chemistry when working with small datasets. Additionally, GP models offer the capability to quantify the uncertainty of each prediction, providing deeper insights into the model's limitations. It is also shown that the inference with GP-based models is slower than ANNs with speed-up factors of 1.9-2.1 relative to the 0D reactor model, whereas the ANN speed-up factor goes up to 3.0.

关键词： probabilistic machine learning Gaussian Processes Artificial neural networks Hydrogen auto-ignition Uncertainty quantification (UQ)

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probabilistic machine learning for detection of tightening torque in bolted joints

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2022年第5期21卷 2136-2151页

作者： Miguel, Luccas P. Teloli, Rafael de O. da Silva, Samuel Chevallier, Gael Univ Estadual Paulista Dept Engn Mecan Fac Engn Campus Ilha Solteira Ilha Solteira Brazil Univ Bourgogne Franche Comte Dept Mecan Appl Besancon Bourgogne Franc France

Observing the loss of tightening torque using modal parameters is challenging due to the variability and nonlinear effects in bolted joints. Thus, this paper proposes a combined application of two probabilistic machine learning methods. First, a Gaussian mixture model (GMM) is learned using estimated natural frequencies, assuming the tightening torque in a safe situation. This probabilistic model can assuredly detect the lack of torque using indirect vibration measures in other unknown states by computing a damage index. A Gaussian process regression (GPR) is also learned considering a set of torque and damage index pairs in several conditions. The GPR model interpolates a curve to supply an estimative of the tightening torque for other conditions not used in this learning. An illustrative application is performed considering the Orion beam, an academic-scale specimen composed of a lap-joint configuration that retains the friction surface in contact patches. The structure is subjected to a random vibration with a controlled RMS level and several tightening torque conditions to identify the modal parameters. The probabilistic model learning via the GMM and GPR can detect adequately, with a low number of false diagnoses, the actual state of torque using an indirect measure of vibration, that is, without the need for a torque sensor on each bolt.

关键词： Bolted joints tightening torque probabilistic machine learning Gaussian Mixture Model Gaussian Process Regression

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Predicting in vitro assays related to liver function using probabilistic machine learning

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Toxicology 2025年 516卷 154195页

作者： Flavio M Morelli Marian Raschke Natalia Jungmann Michaela Bairlein Marina García de Lomana R&D Machine Learning Research Bayer AG Pharmaceuticals Division Berlin Germany Department of Mathematics and Computer Science Free University of Berlin Berlin Germany. Electronic address: flavio.morelli@fu-berlin.de. R&D Preclinical Development Bayer AG Pharmaceuticals Division Berlin Germany. R&D Machine Learning Research Bayer AG Pharmaceuticals Division Berlin Germany.

While machine learning has gained traction in toxicological assessments, the limited data availability requires the quantification of uncertainty of in silico predictions for reliable decision-making. This study addresses the challenge of predicting the outcome of in vitro assays associated with liver function by systematically comparing various probabilistic methods. Our research fills a critical gap by integrating multiple data modalities - chemical descriptors, gene expression, and morphological profiles - into a probabilistic framework aimed at predicting in vitro assays and quantifying uncertainty. We present a comprehensive evaluation of the performance of these data modalities and describe how this framework and the in vitro assay predictions can be integrated to estimate the probability of drug-induced liver injury (DILI) occurrence. Additionally, we contribute new experimental data for reactive oxygen species generation and hepatocyte toxicity assays, providing valuable resources for future research. Our findings underscore the importance of incorporating uncertainty quantification in toxicity predictions, potentially leading to a safer drug development process and reduced reliance on animal testing.

关键词： Bayesian Modeling Hepatotoxicity In Vitro Assays Multimodal Integration probabilistic machine learning Uncertainty Quantification

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probabilistic machine learning based predictive and interpretable digital twin for dynamical systems

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COMPUTERS & STRUCTURES 2023年第1期281卷

作者： Tripura, Tapas Desai, Aarya Sheetal Adhikari, Sondipon Chakraborty, Souvik Indian Inst Technol Delhi Dept Appl Mech Delhi 110016 India Indian Inst Technol Delhi Yardi Sch Artificial Intelligence ScAI Delhi 110016 India Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland

A framework for creating and updating digital twins for dynamical systems from a library of physics -based functions is proposed. The sparse Bayesian machine learning is used to update and derive an inter-pretable expression for the digital twin. Two approaches for updating the digital twin are proposed. The first approach makes use of both the input and output information from a dynamical system, whereas the second approach utilizes output-only observations to update the digital twin. Both methods use a library of candidate functions representing certain physics to infer new perturbation terms in the existing digital twin model. In both cases, the resulting expressions of updated digital twins are identical, and in addition, the epistemic uncertainties are quantified. In the first approach, the regression problem is derived from a state-space model, whereas in the latter case, the output-only information is treated as a stochastic pro-cess. The concepts of Ito calculus and Kramers-Moyal expansion are being utilized to derive the regres-sion equation. The performance of the proposed approaches is demonstrated using highly nonlinear dynamical systems such as the crack-degradation problem. Numerical results demonstrated in this paper almost exactly identify the correct perturbation terms along with their associated parameters in the dynamical system. The probabilistic nature of the proposed approach also helps in quantifying the uncer-tainties associated with updated models. The proposed approaches provide an exact and explainable description of the perturbations in digital twin models, which can be directly used for better cyber-physical integration, long-term future predictions, degradation monitoring, and model-agnostic control. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Predictive digital twin Model update probabilistic machine learning Stochastic differential equation

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Overcoming the Cold Start Problem of Customer Relationship Management Using a probabilistic machine learning Approach

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JOURNAL OF MARKETING RESEARCH 2021年第5期58卷 981-1006页

作者： Padilla, Nicolas Ascarza, Eva London Business Sch Mkt London England Harvard Sch Business Business Adm Boston MA USA

The success of customer relationship management programs ultimately depends on the firm's ability to identify and leverage differences across customers-a difficult task when firms attempt to manage new customers, for whom only the first purchase has been observed. The lack of repeated observations for these customers poses a structural challenge for firms to infer unobserved differences across them. This is what the authors call the "cold start" problem of customer relationship management, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. The authors propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it flexibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is flexible enough to capture a wide range of heterogeneity structures. The authors validate their approach in a retail context and empirically demonstrate the model's ability to identify high-value customers as well as those most sensitive to marketing actions right after their first purchase.

关键词： customer relationship management deep exponential families probabilistic machine learning cold start problem

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BENCHMARKING probabilistic machine learning MODELS FOR ARCTIC SEA ICE FORECASTING

BENCHMARKING PROBABILISTIC MACHINE LEARNING MODELS FOR ARCTI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ali, Sahara Mostafa, Seraj A. M. Li, Xingyan Khanjani, Sara Wang, Jianwu Foulds, James Janeja, Vandana Univ Maryland Baltimore Cty Baltimore MD 21250 USA

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The Arctic is a region with unique climate features, motivating new AI methodologies to study it. Unfortunately, Arctic sea ice has seen a continuous decline since 1979. This not only poses a significant threat to Arctic wildlife and surrounding coastal communities but is also adversely affecting the global climate patterns. To study the potential of AI in tackling climate change, we analyze the performance of four probabilistic machine learning methods in forecasting sea-ice extent for lead times of up to 6 months, further comparing them with traditional machine learning methods. Our comparative analysis shows that Gaussian Process Regression is a good fit to predict sea-ice extent for longer lead times with lowest RMSE score.

关键词： Arctic sea ice climate change probabilistic machine learning Gaussian Process Regression

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