Recent advances in 4D printing of fiber-reinforced polymer composites: A review and outlook

Composites Part B Engineering, Год журнала: 2024, Номер 283, С. 111645 - 111645

Опубликована: Июль 2, 2024

Язык: Английский

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Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading

Discover Materials, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 11, 2025

Abstract The increasing demand for advanced materials capable of withstanding extreme loading conditions, such as those encountered during impact or blast events, underscores the need innovative approaches in material processing. This study focuses on leveraging machine learning (ML) to enhance predictive accuracy continuous extrusion CP-Titanium Grade 2, a vital structural resilience critical applications. Specifically, an Artificial Neural Network (ANN) model optimized using Stochastic Gradient Descent (SGD) was introduced forecast power requirements with high precision. analysis utilized published dataset that comprises theoretical, numerical, and experimental calculations robust foundation validation comparison. A visualization highlighted influence process parameters, feedstock temperature wheel velocity, performance align thematic focus resilient design. ANN-SGD achieved RMSE 0.9954 CVRMSE 11.53% which demonstrated significant improvements prediction compared traditional approaches. By achieving superior alignment results, validated its efficacy reliable efficient tool understanding optimizing complex manufacturing processes. research emphasizes potential ML revolutionize processing conditions contribute broader goals sustainable manufacturing.

Язык: Английский

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Advanced 3D-printed PVDF/BT piezoelectric energy harvester with a bio-inspired 3D structure for a self-powered smart mouse

Nano Energy, Год журнала: 2024, Номер 128, С. 109876 - 109876

Опубликована: Июнь 10, 2024

Язык: Английский

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Designing advanced 4D printing thermo-sensitive shape memory polymer blends for enhanced mechanical and shape memory performances

Progress in Additive Manufacturing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Язык: Английский

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Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications

Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials, Год журнала: 2025, Номер 166, С. 106949 - 106949

Опубликована: Фев. 25, 2025

Язык: Английский

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Advanced nanocomposites for 4D printing: High-performance electroactive shape memory polymers for smart applications

Applied Materials Today, Год журнала: 2025, Номер 44, С. 102702 - 102702

Опубликована: Апрель 3, 2025

Язык: Английский

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Enhanced piezoelectric performance of PVDF/MWCNTs energy harvester through a 3D-printed multimodal auxetic structure for smart security systems

Materials Today Sustainability, Год журнала: 2024, Номер 27, С. 100847 - 100847

Опубликована: Май 20, 2024

Язык: Английский

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A review of machine learning in additive manufacturing: design and process

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 135(3-4), С. 1051 - 1087

Опубликована: Окт. 5, 2024

Язык: Английский

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Artificial Intelligence and Machine Learning Approaches in Eco-Design for Additive Manufacturing: A Literature Review

Lecture notes in mechanical engineering, Год журнала: 2025, Номер unknown, С. 371 - 380

Опубликована: Янв. 1, 2025

Язык: Английский

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Sustainable and Optimized Production in an Aluminum Extrusion Process

Sustainability, Год журнала: 2025, Номер 17(9), С. 4179 - 4179

Опубликована: Май 6, 2025

In discussions on environmental policies, eco-efficiency is often underlined. Eco-efficiency defined as delivering products and services with competitive value while simultaneously reducing the ecological impacts meeting human needs. highly industrial environments, improvements in production processes are crucial for maintaining a strong differentiated position ability. Additionally, rationalizing energy consumption optimizing use of natural resources essential sustainability. This work presents an empirical study Portuguese company focused minimizing scrap extrusion processes. common challenge worldwide, significant economic implications. A literature review revealed relationships between key process parameters, including temperature, time, speed, pressure, geometry. The main objective this to model aluminum simple replicable way, avoiding complex models such nonlinear optimization or finite element methods, view toward potential machine learning applications reduction. Thus, multiple linear regression enable identification most influential variables involved process. results identify that impact generation, aligning findings from literature. dataset, geometry-related factors parameters notable rates.

Язык: Английский

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