Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Massaro, Alessandro

doi:10.3390/electronics12183772

Cited by 7 publications

(3 citation statements)

References 267 publications

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“…When the material is mechanically damaged, the internal chemical bonds will break. Then, they can repair the broken structure through bond recombination and mutual forces to achieve various reversible healings, which is more suitable for the long-term stable working performance of e-skin [78]. Han et al recorded the self-healing results of polyacrylic gelatin sodium chloride hydrogel (PAA gelatin NaCl hydrogel).…”

Section: Self-healing and Degradabilitymentioning

confidence: 99%

Recent Advances in Self-Powered Electronic Skin Based on Triboelectric Nanogenerators

Feng,

Wen,

Sun

et al. 2024

Energies

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Human skin, the body’s largest organ, plays a crucial role in perceiving mechanical stimulation and facilitating interaction with the external environment. Leveraging the unique attributes of human skin, electronic skin technology aimed at replicating and surpassing the capabilities of natural skin holds significant promise across various domains, including medical care, motion tracking, and intelligent robotics. In recent research, triboelectric nanogenerators have emerged as a compelling solution for addressing the energy challenge in electronic skins. Triboelectric nanogenerators harness the combination of the triboelectric effect and electrostatic induction to efficiently convert mechanical energy into electrical power, serving as self-powered sensors for electronic skins, which possess the advantages of self-powered operation, cost-effectiveness, and compatibility with a wide range of materials. This review provides an introduction to the working principles and the four operational modes of triboelectric nanogenerators, highlighting the functional features of electronic skins, such as stretchability, self-healing, and degradability. The primary focus is on the current applications of self-powered electronic skins based on triboelectric nanogenerators in medical care, motion tracking, and machine tactile recognition. This review concludes by discussing the anticipated challenges in the future development of self-powered electronic skins based on triboelectric nanogenerators. This review holds practical significance for advancing the practical use of self-powered electronic skins based on triboelectric nanogenerators and offers valuable guidance for individuals interested in pursuing scientific and healthy endeavors.

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Section: Self-healing and Degradabilitymentioning

confidence: 99%

Recent Advances in Self-Powered Electronic Skin Based on Triboelectric Nanogenerators

Feng,

Wen,

Sun

et al. 2024

Energies

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“…Advances in materials and methods are expanding applications. Smart materials that respond to stimuli and advanced composites are creating new capabilities for sensing, actuation, and adjustable properties in manufactured components (Massaro et al, 2023). Nanomaterials also offer enhanced mechanical, electrical, optical, thermal, or biological properties (Su and Song, 2021).…”

Section: Introductionmentioning

confidence: 99%

Advance Manufacturing Technology and Heavy Machinery of Industry

2024

IRJMETS

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Advancements in advanced manufacturing technologies like automation, robotics, artificial intelligence, and additive manufacturing are transforming industrial production capabilities. However, heavy machinery remains essential for materials processing, construction, fluid handling, and other core manufacturing operations. This review synthesizes current literature on the integration of emerging technologies with conventional heavy equipment to identify synergistic opportunities, implementations, benefits, and challenges. A systematic search of engineering databases yielded 15 relevant studies on technology adoption in manufacturing sectors utilizing heavy machinery. Key findings indicate that innovations in precision control, connectivity, and data-driven functionality have enhanced the capabilities of equipment like metal cutting and forming tools, pumps, construction machinery, and packaging systems. Successful integration examples showcase increased productivity, flexibility, predictive maintenance, and end-to-end supply chain coordination. However, adoption barriers include upfront costs, skills gaps, and change management needs. In the future, advance manufacturing will require complementary leveraging of technological innovation and heavy machinery. More research is needed on areas like human-machine collaboration, rapid reconfigurable robotics, and digital twin integration. This review provides insights on current progress and future directions at the nexus of advanced technologies and industrial equipment.

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“…Furthermore, ANNs are useful to automate circuit design [9] and electronic controlled systems [10,11]. A particular application of ANNs is in detecting and classifying parametric/ soft faults affecting analog integrated circuits [12,13].…”

Section: Introductionmentioning

confidence: 99%

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Massaro

2024

Self Cite

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In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.

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Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Cited by 7 publications

References 267 publications

Recent Advances in Self-Powered Electronic Skin Based on Triboelectric Nanogenerators

Recent Advances in Self-Powered Electronic Skin Based on Triboelectric Nanogenerators

Advance Manufacturing Technology and Heavy Machinery of Industry

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

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