Polyesterimide composites as a sensor material for sliding bearings

Rakowski, W.; Zimowski, S.

doi:10.1016/j.compositesb.2005.09.002

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Other researchers have also shown the usefulness of using neural networks for analysing the information furnished by sensors that monitor the behaviour of complex systems. Anomaly detection has been applied for a realtime prediction of the behaviour of the system being monitored and thus locate any problems by comparing the measurements recorded by the sensors [27,28].…”

Section: Artificial Neural Network For Modelling Complex Systemsmentioning

confidence: 99%

Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments

Lantada¹,

Morgado²,

Sanz³

et al. 2012

JSIP

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Ionic polymer-metal composites (IPMCs) are especially interesting electroactive polymers because they show large a deformation in the presence of a very low driving voltage (around 1 - 2 V) and several applications have recently been proposed. Normally a humid environment is required for the best operation, although some IPMCs can operate in a dry environment, after proper encapsulation or if a solid electrolyte is used in the manufacturing process. However, such solutions usually lead to increasing mechanical stiffness and to a reduction of actuation capabilities. In this study we focus on the behaviour of non-encapsulated IPMCs as actuators in dry environments, in order to obtain relevant information for design tasks linked to the development of active devices based on this kind of smart material. The non-linear response obtained in the characterisation tests is especially well-suited to modelling these actuators with the help of artificial neural networks (ANNs). Once trained with the help of characterisation data, such neural networks prove to be a precise simulation tool for describing IPMC response in dry environments

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Section: Artificial Neural Network For Modelling Complex Systemsmentioning

confidence: 99%

Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments

Lantada¹,

Morgado²,

Sanz³

et al. 2012

JSIP

View full text Add to dashboard Cite

show abstract

“…Other researchers have also shown the usefulness of using neural networks to analyse information from sensors monitoring the behaviour of complex systems. Such networks have also been used to find anomalies when making a realtime prediction of the system being monitored and to detect problems by making comparisons with the measurements recorded by the sensors (Gulati andZak 2000, Rakowski andZimowski 2006). They have also proven to be of use for processing the information from sensors (or sensor networks) that are sensitive to different stimuli and recognizing the kind of stimulus that has generated a specific signal, as has been described for mobile robots with audio-tactile micro-sensors (Manoonpong et al 2004) and for robots with multiple sensors (Stiehl et al 2005).…”

Section: Neural Network As a Modelling Tool: Possible Advantages For ...mentioning

confidence: 99%

Neural network approach to modelling the behaviour of quantum tunnelling composites as multifunctional sensors

Lantada¹,

Morgado²,

Otero³

et al. 2010

Smart Mater. Struct.

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Quantum tunnelling composites, or 'QTCs', are composites with an elastomeric polymer matrix and a metal particle filling (usually nickel). At rest, these metal particles do not touch each other and the polymer acts as an insulator. When the material is suitably deformed, however, the particles come together (without actually touching) and the quantum tunnelling effect is promoted, which causes the electrical resistance to fall drastically. This paper contains a detailed description of neural networks for a faster, simpler and more accurate modelling and simulation of QTC behaviour that is based on properly training these neural models with the help of data from characterization tests. Instead of using analytical equations that integrate different quantum and thermomechanical effects, neural networks are used here due to the notable nonlinearity of the aforementioned effects, which involve developing analytical models that are too complex to be of practical use. By conducting tests under different pressures and temperatures that encompass a wide range of operating conditions for these materials, different neural networks are trained and compared as the number of neurons is increased. The results of these tests have also enabled certain previously described phenomena to be simulated with more accuracy, especially those involving the response of QTCs to changes in pressure and temperature.

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“…In the last decade, polymeric composites have been studied for the optimization of the intrinsic properties of their individual constituents to obtain special properties for both different and specific applications of these composites. The filler addition into the polymeric matrix causes important changes in the behavior of the mechanical properties [1][2][3], thermal properties [3][4][5], and electrical properties [6,7] of the composites. The electrically conducting composites possess various advantages when they are compared with their pure metallic counterpart: low cost, simple production, low weight, capacity to absorb the mechanical impact, resistance, and the feasibility of adjusting the electrical conductivity as a function of the filler concentration [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the fiber insertion of metallic spheres of micrometric size into polymer matrices has reached conductivities of practical use [2,6,[22][23][24]; nevertheless, the fragility of the polymeric composite increases drastically, and the central problem persists: the homogeneity, the critical concentration of metallic particles, and the morphology are insufficient to obtain acceptable both mechanical and electrical properties.…”

Section: Introductionmentioning

confidence: 99%

Mechanical, Electrical, and Glass Transition Behavior of Copper–PMMA Composites

Poblete

Alvarez

2023

Crystals

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The mechanical, electrical, and glass transition behaviors (Tg) of polymethylmethacrylate (PMMA)–metal systems have been studied. Considering both the particle size and the metal filler concentration, the electrical conductivity showed a clear dependence on the sample thickness to reach percolation. An increase of up to 400% of strain-to-failure for the 2% v/v of nanometric filler composites in the mechanical test was observed. Tg analysis showed a decrease in the glass transition temperature when the increase of nanometric metallic filler reached the limit of 2% v/v. Over this concentration, the Tg values showed a tendency to reach the original value of the polymeric matrix without conductive filler. For the 20% v/v micrometric filler composites, the strain-to-failure increased up to 58%, but in the Tg analysis of this composite, no relevant changes were observed when the micrometric metallic filler was increased.

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Polyesterimide composites as a sensor material for sliding bearings

Cited by 10 publications

References 17 publications

Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments

Neural Network Approach to Modelling the Behaviour of Ionic Polymer-Metal Composites in Dry Environments

Neural network approach to modelling the behaviour of quantum tunnelling composites as multifunctional sensors

Mechanical, Electrical, and Glass Transition Behavior of Copper–PMMA Composites

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