Physical and mechanical properties of PVDF/KNN composite produced via hot compression molding

Mirzazadeh, Z.; Sherafat, Z.; Bagherzadeh, Elham

doi:10.1016/j.ceramint.2020.10.199

Cited by 36 publications

(11 citation statements)

References 43 publications

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“…Furthermore, the crystal phase of KNN can be judged by observing the intensities of two splitting peaks appearing around 461. 34 From the enlarged area of the diffraction peak at 461 on the right side of Fig. 3(a), it can be seen that the intensity of the (200) crystal plane is smaller than that of the (002) crystal plane, which proves that KNN belongs to the orthorhombic crystal system.…”

Section: Measurements and Characterizationsmentioning

confidence: 93%

Graphene doping to enhance the mechanical energy conversion performances of GR/KNN/P(VDF-TrFE) flexible piezoelectric sensors

Zhang

Xia

Cao

et al. 2023

Phys. Chem. Chem. Phys.

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Section: Measurements and Characterizationsmentioning

confidence: 93%

Graphene doping to enhance the mechanical energy conversion performances of GR/KNN/P(VDF-TrFE) flexible piezoelectric sensors

Zhang

Xia

Cao

et al. 2023

Phys. Chem. Chem. Phys.

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“…Lead‐free1 piezoelectric ceramics have attracted more and more attention owing to the restriction of lead‐based piezoelectric ceramics because of the Pb in lead zirconate titanate (PZT) piezoelectric ceramics released during manufacturing, use, and disposal, which may cause serious damage to people and environment 1–9 . (K, Na) NbO 3 (KNN) is an important lead‐free piezoelectric ceramic with high Curie temperature ( T C ) and excellent electrical properties, and it is identified as one of the most likely candidates to replace the PZT 10–13 . It is of profound significance to carry out more research on the KNN lead‐free piezoelectric ceramics.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9] (K, Na) NbO 3 (KNN) is an important lead-free piezoelectric ceramic with high Curie temperature (T C ) and excellent electrical properties, and it is identified as one of the most likely candidates to replace the PZT. [10][11][12][13] It is of profound significance to carry out more research on the KNN lead-free piezoelectric ceramics.…”

Section: Introductionmentioning

confidence: 99%

Effect of a lattice distortion strategy on the phase transition and properties in KNN‐based ceramics

Sun

et al. 2022

J Am Ceram Soc.

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High performance lead‐free piezoelectric ceramics are of great importance to the sustainable development of the environment. To obtain excellent comprehensive performance KNN‐based lead‐free piezoelectric ceramics, a lattice distortion strategy combined with domain configuration was designed in (1 − x)K0.5Na0.5Nb0.95Sb0.05O3–xCaHfO3 ((1 − x)KNNS–xCH) system by introduced Ca2+ into the A‐site and Hf4+ into the B‐site. The results demonstrated that the rhombohedral–orthorhombic–tetragonal polymorphic phase boundary (PPB) was constructed in 0.02 ≤ x ≤ 0.04 and significant lattice distortion occurred in R‐ and T‐phase. Moreover, the 0.97KNNS–0.03CH sample exhibited excellent electrical performance (e.g., kp ∼ 43.8%, d*33 ∼ 478.6 pm/V, and d33 ∼ 392 pC/N) together with a high Curie temperature (TC ∼ 295°C) profited from the PPB and domain configurations. The ceramics also showed the optimal thermal stability, which was beneficial to promote the development of KNN‐based ceramics.

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“…For its great algorithm to solve the problem of nonlinear regression and excellent prediction accuracy of material rheological behavior, machine learning provided a new idea and method for the establishment of material constitutive models. The current machine learning models for flow stress prediction include artificial neural network [15][16][17][18][19], support vector machine regression [20,21] and K-nearest neighbor regression [22], etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of high-temperature flow stress of HMn64–8–5–1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

Qiang

Zhang

Liu

et al. 2022

Mater. Res. Express

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An accurate constitutive model is essential for designing the process of hot precision forging and numerical simulation. Based on the isothermal compression tests of as-extruded HMn64-8-5-1.5 manganese brass alloy at the deformation temperature of 873-1073 K and strain rate of 0.01-10 s-1, the effect of the friction and deformation temperature rise on the flow stress during the hot compression process was analyzed, and the flow stress curves were corrected. Three constitutive models based on the modified Zerilli-Armstrong, Arrhenius, and a back-propagation neural network (BPNN) optimized by the grey wolf optimization (GWO) algorithm (GWO-BPNN) models were established to describe the high-temperature flow stress of this alloy. Meanwhile, the prediction ability of the three models was evaluated by the calculated values of mean absolute percentage error (MAPE) and root mean square error (RMSE). The values of MAPE for the modified Zerilli-Armstrong, Arrhenius, and GWO-BPNN models were computed to be, 3.139 %, 2.448 % and 1.265 %, and the values of RMSE were calculated to be 1.804, 1.482 and 0.467 MPa, respectively. The GWO-BPNN model was with the greatest prediction ability for the flow stress among these models. The GWO algorithm was introduced to optimize the initial weights and thresholds of the BPNN model, and it has good prediction accuracy and better stability. It can better describe the high-temperature flow behavior of HMn64-8-5-1.5 alloy.

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Physical and mechanical properties of PVDF/KNN composite produced via hot compression molding

Cited by 36 publications

References 43 publications

Graphene doping to enhance the mechanical energy conversion performances of GR/KNN/P(VDF-TrFE) flexible piezoelectric sensors

Graphene doping to enhance the mechanical energy conversion performances of GR/KNN/P(VDF-TrFE) flexible piezoelectric sensors

Effect of a lattice distortion strategy on the phase transition and properties in KNN‐based ceramics

Prediction of high-temperature flow stress of HMn64–8–5–1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

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