Machine vision and novel attention mechanism TCN for enhanced prediction of future deposition height in directed energy deposition

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 216, P. 111492 - 111492

Published: May 3, 2024

Language: Английский

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Intelligently optimized arch-honeycomb metamaterial with superior bandgap and impact mitigation capacity

Composites Part A Applied Science and Manufacturing, Journal Year: 2024, Volume and Issue: 185, P. 108298 - 108298

Published: June 7, 2024

Language: Английский

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Machine learning in prediction of residual stress in laser shock peening for maximizing residual compressive stress formation

Materials & Design, Journal Year: 2024, Volume and Issue: 243, P. 113079 - 113079

Published: June 12, 2024

Laser Shock Peening (LSP) is an advanced technique for enhancing surface properties, drawing significant interest its ability to induce beneficial residual stresses in materials. Traditional LSP design processes, reliant on manual parameter selection, often result imprecise control over the stress distribution, necessitating multiple iterations and high costs. This study introduces a machine learning (ML)-based approach, utilizing Random Forest (RF) algorithm, automate optimize of parameters nickel-aluminium bronze surfaces. Our findings demonstrate RF model's capability accurately predict distributions, achieving compressive up 472 MPa with notable reduction iterations. The model forecasts both uniform non-uniform patterns, particularly identifying areas susceptible Residual Stress Holes (RSH) improved precision. With Absolute Percentage Error (APE) only 6.2 %, our approach significantly outperforms traditional ML algorithms, offering novel method efficiently designing complex fields applications.

Language: Английский

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Intelligent Design of Low-Frequency Bandgaps in Cementitious Metamaterials for Enhanced Tunability

Thin-Walled Structures, Journal Year: 2024, Volume and Issue: unknown, P. 112860 - 112860

Published: Dec. 1, 2024

Language: Английский

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Efficient Dataset Generation for Inverse Design of Micro-Perforated Sonic Crystals

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 110190 - 110190

Published: March 1, 2025

Language: Английский

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Convolutional neural networks to predict dispersion surfaces-based properties of acoustic metamaterials with arbitrary-shaped unit cells

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104905 - 104905

Published: April 1, 2025

Language: Английский

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The Data-Driven Performance Prediction of Lattice Structures: The State-of-the-Art in Properties, Future Trends, and Challenges

Aerospace, Journal Year: 2025, Volume and Issue: 12(5), P. 390 - 390

Published: April 30, 2025

Lattice structures, with their unique design, offer properties like a programmable elastic modulus, an adjustable Poisson’s ratio, high specific strength, and large surface area, making them the key to achieving structural lightweighting, improving impact resistance, vibration suppression, maintaining thermal efficiency in aerospace field. However, functional prediction inverse design remain challenging due cross-scale effects, extensive spatial freedom, computational costs. Recent advancements AI have driven progress predicting lattice structure functionality. This paper begins introduction types, properties, applications. Then development process for performance-prediction methods of structures is summarized. The current applications methods, which are data-driven related material performance under conditions coupled multi-physical fields, analyzed, this analysis further extends relation summarizes application mechanical, energy absorption, acoustic, structures; elaborates on these optimization field; details relevant theory references field analysis. Finally, problems research demonstrated, future direction envisioned.

Language: Английский

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Data-driven topology optimization design of phononic crystals for vibration control

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: 297-298, P. 110358 - 110358

Published: May 8, 2025

Language: Английский

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Electrically controllable and reversible coupling degree in a phononic crystal with double piezoelectric defects

Thin-Walled Structures, Journal Year: 2024, Volume and Issue: 204, P. 112328 - 112328

Published: Aug. 8, 2024

Language: Английский

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Advances in artificial intelligence for artificial metamaterials

APL Materials, Journal Year: 2024, Volume and Issue: 12(12)

Published: Dec. 1, 2024

The 2024 Nobel Prizes in Physics and Chemistry were awarded for foundational discoveries inventions enabling machine learning through artificial neural networks. Artificial intelligence (AI) metamaterials are two cutting-edge technologies that have shown significant advancements applications various fields. AI, with its roots tracing back to Alan Turing’s seminal work, has undergone remarkable evolution over decades, key including the Turing Test, expert systems, deep learning, emergence of multimodal AI models. Electromagnetic wave control, critical scientific research industrial applications, been significantly broadened by metamaterials. This review explores synergistic integration metamaterials, emphasizing how accelerates design functionality materials, while novel physical networks constructed from enhance AI’s computational speed ability solve complex problems. paper provides a detailed discussion AI-based forward prediction inverse principles metamaterial design. It also examines potential big-data-driven methods addressing challenges In addition, this delves into role advancing focusing on progress electromagnetic optics, terahertz, microwaves. Emphasizing transformative impact intersection between underscores improvements efficiency, accuracy, applicability. collaborative development process opens new possibilities innovations photonics, communications, radars, sensing.

Language: Английский

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