An optimization-centric review on integrating artificial intelligence and digital twin technologies in manufacturing

Engineering Optimization, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 47

Published: Jan. 3, 2025

Language: Английский

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Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis

Sci, Journal Year: 2024, Volume and Issue: 6(4), P. 60 - 60

Published: Oct. 3, 2024

This study explores the evolution and impact of research on challenges opportunities in implementation artificial intelligence (AI) manufacturing between 2019 August 2024. By addressing growing integration AI technologies sector, seeks to provide a comprehensive view how applications are transforming production processes, improving efficiency, opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, thematic trends. Data were collected from Scopus, detailed review publications carried out identify knowledge gaps unresolved questions. The results reveal steady increase related manufacturing, with strong focus automation, predictive maintenance, supply chain optimization. also highlights dominance certain institutions authors driving this field research. Despite progress, significant remain, particularly regarding scalability solutions ethical considerations. findings suggest that while holds considerable potential for industry, more interdisciplinary is needed address existing maximize its benefits.

Language: Английский

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Simulation-in-the-loop additive manufacturing for real-time structural validation and digital twin development

Additive manufacturing, Journal Year: 2025, Volume and Issue: unknown, P. 104631 - 104631

Published: Jan. 1, 2025

Language: Английский

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Data Enabling Technology in Digital Twin and its Frameworks in Different Industrial Applications

Journal of Industrial Information Integration, Journal Year: 2025, Volume and Issue: unknown, P. 100793 - 100793

Published: Feb. 1, 2025

Language: Английский

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Digital twin of dynamics for parallel kinematic machine with distributed force/position interaction

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 70 - 88

Published: March 3, 2025

Language: Английский

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Development of a digital twin system for constructing rough surface of models in light-curing additive manufacturing

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

Light-curing additive manufacturing is extensively employed in high-precision industries due to its capability generate products with exceptional surface quality. Nonetheless, given the susceptibility of light-curing models various factors, current model constructed based on design parameters, which may not precisely replicate actual surface. This constraint hampers analytical work across stages. Moreover, distinct analysis stages necessitate varied physical models. While it feasible produce corresponding for each stage, this approach result time wastage and reduced efficiency. To address issue, paper introduces a digital twin system manufacturing, incorporating an integrated algorithm specifically designed constructing rough surfaces. The employs Fast Fourier Transform (FFT), Johnson Transformation System, autocorrelation Function topography model. Additionally, can monitor printer’s stability throughout printing process. validate feasibility system, DT was implemented process construct printed measured using 3D profiler perform statistical data. Finally compared by system. results indicate that characteristic parameter errors are all below 5%, providing evidence fulfills specified requirements.

Language: Английский

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Uncertainty-Aware Self-Attention Model for Time Series Prediction with Missing Values

Fractal and Fractional, Journal Year: 2025, Volume and Issue: 9(3), P. 181 - 181

Published: March 16, 2025

Missing values in time series data present a significant challenge, often degrading the performance of downstream tasks such as classification and forecasting. Traditional approaches address this issue by first imputing missing then independently solving predictive tasks. Recent methods have leveraged self-attention models to enhance imputation quality accelerate inference. These models, however, predict based on all input observations—including values—thereby potentially compromising fidelity imputed data. In paper, we propose Uncertainty-Aware Self-Attention (UASA) model overcome these limitations. Our approach introduces two novel techniques: (i) A mechanism with partially observed diagonal that effectively captures complex non-local dependencies data—a characteristic also fractional-order systems. This draws inspiration from fractional calculus, where non-integer-order derivatives better characterize dynamical systems long-memory effects, providing more comprehensive mathematical framework for handling temporal And (ii) uncertainty quantification inform The UASA comprises an upstream component prediction, trained jointly end-to-end fashion optimize both accuracy task-specific objectives simultaneously. For tasks, demonstrates remarkable even under high rates, achieving ROC-AUC 99.5%, PR-AUC 58.5%, F1-SCORE 49.3%. forecasting AUST-Gait dataset, achieves Mean Squared Error (MSE) 0.72 0% conditions (i.e., complete input). Under training strategy evaluated across average MSE 0.74, showcasing its adaptability robustness diverse scenarios.

Language: Английский

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Real-time decision-making for Digital Twin in additive manufacturing with Model Predictive Control using time-series deep neural networks

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 412 - 424

Published: March 29, 2025

Language: Английский

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A review on laser cladding with wire feeding: process fundamentals, theoretical analyses, online monitoring, and quality controls

The International Journal of Advanced Manufacturing Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Language: Английский

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Transforming Additive Manufacturing with Artificial Intelligence: A Review of Current and Future Trends

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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