Application of machine learning for improved accuracy of simultaneous temperature and strain measurements of carbon fiber-reinforced plastic laminates using an embedded tilted fiber Bragg grating sensor

Kikuchi, Mikiko; Ogasawara, Toshio; Fujii, Shunpei; Takeda, Shin‐ichi

doi:10.1016/j.compositesa.2022.107108

Cited by 16 publications

(4 citation statements)

References 24 publications

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“…The “multiresonant” approach, naturally resulting from the hybrid nature of the spectral content featuring well-resolved S and P cladding mode resonance families, is ideally suited to the development of more advanced data extraction algorithms to extract target measurements from the simultaneous values of multiple parameters of the spectra, − using least-squares optimization techniques or machine learning tools. − It is hoped that these simplified fiber SPR probes, along with less complex interrogation instrumentation, will stimulate new efforts into developing lab-on-fiber tools for field applications.…”

Section: Discussionmentioning

confidence: 99%

Dual-Mode Comb Plasmonic Optical Fiber Sensing

Villatoro,

Loyez,

Villatoro

et al. 2024

ACS Sens.

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Section: Discussionmentioning

confidence: 99%

Dual-Mode Comb Plasmonic Optical Fiber Sensing

Villatoro,

Loyez,

Villatoro

et al. 2024

ACS Sens.

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“…Monitoring the manufacturing process usually involves detecting anomalies and measuring physical quantities such as pressure, temperature etc., which can be easily captured using rGO sensors. The real-time processing of signals with very low computational power makes these tools very attractive 127 , 128 . The signals measured by rGO-based sensors would normally be in the form of resistance/voltage/current measurements.…”

Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 99%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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One of the potential applications of 2D materials is to enhance multi-functionality of structures and components used in aerospace, automotive, civil and defense industries. These multi-functional attributes include sensing, energy storage, EMI shielding and property enhancement. In this article, we have explored the potential of using graphene and its variants as data generating sensory elements in Industry 4.0. We have presented a complete roadmap to cover three emerging technologies i.e. advance materials, artificial intelligence and block-chain technology. The utility of 2D materials such as graphene nanoparticles is yet to be explored as an interface for digitalization of a modern smart factory i.e. “factory-of-the-future”. In this article, we have explored how 2D material enhanced composites can act as an interface between physical and cyber spaces. An overview of employing graphene-based smart embedded sensors at various stages of composites manufacturing processes and their application in real-time structural health monitoring is presented. The technical challenges associated with interfacing graphene-based sensing networks with digital space are discussed. Additionally, an overview of the integration of associated tools such as artificial intelligence, machine learning and block-chain technology with graphene-based devices and structures is also presented.

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“…However, because optical fibers are sensitive to both strain and temperature, it is necessary to compensate for the effects of temperature variation due to the surrounding environment when obtaining the mechanical strain. Therefore, various methods of separating the effects of strain and temperature have been studied, such as the superimposed fiber Bragg grating (FBG) [ 3 ], hybrid FBG [ 4 ], superstructure FBG [ 5 ], FBG with different diameters [ 6 ], single-mode fiber-based up-taper–down-taper–up taper (UDU) structure [ 7 ], sidelobe power of the FBG [ 8 ], and tilted FBG [ 9 , 10 ]. In SHM, distributed measurements are particularly beneficial.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Measurement of Strain and Temperature Distributions Using Optical Fibers with Different GeO2 and B2O3 Doping

Hisada

Kodakamine

Wada

et al. 2023

Sensors

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Compensating for the effects of temperature is a crucial issue in structural health monitoring when using optical fiber sensors. This study focused on the change in sensitivity due to differences in GeO2 and B2O3 doping and then verified the accuracy when measuring the strain and temperature distributions simultaneously. Four types of optical fiber sensors were utilized to measure the strain and temperature in four-point bending tests, and the best combination of the sensors resulted in strain and temperature errors of 28.4 μϵ and 1.52 °C, respectively. Based on the results obtained from the four-point bending tests, we discussed the error factors via an error propagation analysis. The results of the error propagation analysis agreed well with the experimental results, thus indicating the effectiveness of the analysis as a method for verifying accuracy and error factors.

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Application of machine learning for improved accuracy of simultaneous temperature and strain measurements of carbon fiber-reinforced plastic laminates using an embedded tilted fiber Bragg grating sensor

Cited by 16 publications

References 24 publications

Dual-Mode Comb Plasmonic Optical Fiber Sensing

Dual-Mode Comb Plasmonic Optical Fiber Sensing

Graphene nanoparticles as data generating digital materials in industry 4.0

Simultaneous Measurement of Strain and Temperature Distributions Using Optical Fibers with Different GeO2 and B2O3 Doping

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