Integrating Artificial Intelligence in Nanomembrane Systems for Advanced Water Desalination

Results in Engineering, Год журнала: 2024, Номер 24, С. 103321 - 103321

Опубликована: Ноя. 6, 2024

Язык: Английский

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Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V

Journal of Manufacturing Systems, Год журнала: 2024, Номер 76, С. 133 - 157

Опубликована: Июль 30, 2024

Язык: Английский

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Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions

Reliability Engineering & System Safety, Год журнала: 2024, Номер 253, С. 110549 - 110549

Опубликована: Окт. 5, 2024

Язык: Английский

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An intelligent prediction paradigm for milling tool parameters design based on multi-task tabular data deep transfer learning integrating physical knowledge

Journal of Manufacturing Processes, Год журнала: 2025, Номер 134, С. 998 - 1020

Опубликована: Янв. 1, 2025

Язык: Английский

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Integrated Multi-strategy Sand Cat Swarm Optimization for Path Planning Applications

Intelligent Systems with Applications, Год журнала: 2025, Номер unknown, С. 200486 - 200486

Опубликована: Янв. 1, 2025

Язык: Английский

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Adaptive process parameters decision-making in robotic grinding based on meta-reinforcement learning

Journal of Manufacturing Processes, Год журнала: 2025, Номер 137, С. 376 - 396

Опубликована: Фев. 8, 2025

Язык: Английский

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Knowledge-Based Adaptive Design of Experiments (KADoE) for Grinding Process Optimization Using an Expert System in the Context of Industry 4.0

Journal of Manufacturing and Materials Processing, Год журнала: 2025, Номер 9(2), С. 62 - 62

Опубликована: Фев. 17, 2025

The integration of human–cyber–physical systems (HCPSs), IoT, digital twins, and big data analytics underpins Industry 4.0, transforming traditional manufacturing into smart with capabilities for real-time monitoring, quality assessment, anomaly detection. A key advancement is the transition from static to adaptive design experiments (DoE), using iterative refinement. This paper introduces an innovative DoE embedded in expert system grinding, combining data-driven knowledge-based methodologies. KSF Grinding Expert™ recommends optimized grinding control variables, guided by a multi-objective optimization framework Non-dominated Sorting Genetic Algorithm II (NSGA-II) Gray Relational Analysis (GRA). rule-based iteratively refines recommendations through feedback historical insights, reducing number trials excluding suboptimal parameters. case study validates approach, demonstrating significant enhancements process efficiency precision. strategy reduces experimental trials, adapts according different processes, can prevent critical defects such as surface cracks. In study, results which are offered validated over 90% accuracy incorporated system’s knowledge base, enabling continuous improvement reduced experimentation future iterations.

Язык: Английский

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Systematic Review of Artificial Intelligence, Machine Learning, and Deep Learning in Machining Operations: Advancements, Challenges, and Future Directions

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 30, 2025

Язык: Английский

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Improved chaotic fruit fly optimization with elman neural network based data-driven approach on product reviews classification

Alexandria Engineering Journal, Год журнала: 2025, Номер 127, С. 628 - 641

Опубликована: Май 20, 2025

Язык: Английский

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Intelligent modeling and detection in grinding: a review of advances, challenges, and prospects

The International Journal of Advanced Manufacturing Technology, Год журнала: 2025, Номер unknown

Опубликована: Июнь 4, 2025

Язык: Английский

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