Impact of printing orientation on inter and intra-layer bonds in 3D printed thermoplastic elastomers: A study using acoustic emission and tensile tests

Adrover-Monserrat, Bàrbara; García-Vilana, Silvia; Sánchez-Molina, David; Llumà, Jordi; Jerez-Mesa, Ramón; Martínez-González, Eva; Travieso-Rodríguez, J. Antonio

doi:10.1016/j.polymer.2023.126241

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…AEs are currently used in many areas of research, quality control and industry in general. AEs have been used in the study of different materials: metals [8][9][10][11][12][13], composites [14,15], additive manufactured materials [16] and human and other living being tissues [17,18]. Other applications are real-time leak detection and localisation [19,20], tool-wear monitoring and other studies on machining processes [21][22][23][24], the health monitoring of large structures [25,26], the study of cavitation and its erosion and the monitoring of fatigue crack onset and growth.…”

Section: Acoustic Emissionmentioning

confidence: 99%

Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Fernández-Osete,

Bermejo,

Ayneto-Gubert

et al. 2024

Foundations

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Nowadays, hydropower plants are being used to compensate for the variable power produced by the new fluctuating renewable energy sources, such as wind and solar power, and to stabilise the grid. Consequently, hydraulic turbines are forced to work more often in off-design conditions, far from their best efficiency point. This new operation strategy increases the probability of erosive cavitation and of hydraulic instabilities and pressure fluctuations that increase the risk of fatigue damage and reduce the life expectancy of the units. To monitor erosive cavitation and fatigue damage, acoustic emissions induced by very-high-frequency elastic waves within the solid have been traditionally used. Therefore, acoustic emissions are becoming an important tool for hydraulic turbine failure detection and troubleshooting. In particular, artificial intelligence is a promising signal analysis research hotspot, and it has a great potential in the condition monitoring of hydraulic turbines using acoustic emissions as a key factor in the digitalisation process. In this paper, a brief introduction of acoustic emissions and a description of their main applications are presented. Then, the research works carried out for cavitation and fracture detection using acoustic emissions are summarised, and the different levels of development are compared and discussed. Finally, the role of artificial intelligence is reviewed, and expected directions for future works are suggested.

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Section: Acoustic Emissionmentioning

confidence: 99%

Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Fernández-Osete,

Bermejo,

Ayneto-Gubert

et al. 2024

Foundations

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show abstract

“…In the framework of biomechanical applications, the AE technique has been used to analyze the fracture of bone and other biological materials; however, there are few quantitative models which use AE data for the prediction of fracture [27,29,28,30]. The novel part of this work lies in the stochastic model presented in section 2.2, whose application to experimental data is discussed in section 3.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission applied to stochastic modeling of microdamage in compact bone

Sanchez-Molina,

García-Vilana

2024

Preprint

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Exploring the stochastic intricacies of bone microstructure is a promising way to make progress on the practical issue of bone fracture. This study investigates the fracture of human complete ribs subjected to bending and using Acoustic Emission (AE) for microfailure detection. As the strain increases, the number of AE signals per unit of time rises until, beyond a certain threshold, an avalanche of signals occurs, indicating the aggregation of numerous microfailures into a macroscopic fracture. Since microfailures appear randomly throughout the bending test, and given the lack of a deterministic law and the random nature of microfailures during the bending test, we opted to develop a stochastic model to account for their occurrence within the irregular and random microstructure of the cortical bone. Notable discoveries encompass the significant correlation between adjusted parameters of the stochastic model and the total number of microfailures with anthropometric variables such as age and body mass index (BMI). The progression of microfailures with strain is significantly more pronounced with age and BMI, as measured by the rate of bone deterioration. In addition, the rate of microfailures is significantly impacted by BMI alone. It is also observed that the average energy of the identified AE events adheres to a precisely defined Pareto distribution for every specimen, with the principal exponent exhibiting a significant correlation with anthropometric variables. From a mathematical standpoint, the model can be described as a double Cox stochastic and explosive (coxplosive process) model. This further provides insight into the reason why the ribs of older individuals are considerably less resilient than those of younger individuals, breaking under a considerably lower maximum strain εmax).

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Acoustic emission applied to stochastic modeling of microdamage in compact bone

Sánchez-Molina,

García-Vilana

2024

Biomech Model Mechanobiol

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Exploring the stochastic intricacies of bone microstructure is a promising way to make progress on the practical issue of bone fracture. This study investigates the fracture of human complete ribs subjected to bending and using acoustic emission (AE) for microfailure detection. As the strain increases, the number of AE signals per unit of time rises until, beyond a certain threshold, an avalanche of signals occurs, indicating the aggregation of numerous microfailures into a macroscopic fracture. Since microfailures appear randomly throughout the bending test, and given the lack of a deterministic law and the random nature of microfailures during the bending test, we opted to develop a stochastic model to account for their occurrence within the irregular and random microstructure of the cortical bone. Notable discoveries encompass the significant correlation between adjusted parameters of the stochastic model and the total number of microfailures with anthropometric variables such as age and body mass index (BMI). The progression of microfailures with strain is significantly more pronounced with age and BMI, as measured by the rate of bone deterioration. In addition, the rate of microfailures is significantly impacted by BMI alone. It is also observed that the average energy of the identified AE events adheres to a precisely defined Pareto distribution for every specimen, with the principal exponent exhibiting a significant correlation with anthropometric variables. From a mathematical standpoint, the model can be described as a double Cox stochastic and explosive (coxplosive process) model. This further provides insight into the reason why the ribs of older individuals are considerably less resilient than those of younger individuals, breaking under a considerably lower maximum strain ($$\varepsilon _{\max }$$ ε max ).

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Impact of printing orientation on inter and intra-layer bonds in 3D printed thermoplastic elastomers: A study using acoustic emission and tensile tests

Cited by 6 publications

References 42 publications

Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Review of the Uses of Acoustic Emissions in Monitoring Cavitation Erosion and Crack Propagation

Acoustic emission applied to stochastic modeling of microdamage in compact bone

Acoustic emission applied to stochastic modeling of microdamage in compact bone

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