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Abstract

In order to improve the evaluation score and accuracy of ceramic 3D printing sample data, a fuzzy algorithm based evaluation method of ceramic 3D printing sample data is proposed. The data of ceramic 3D printing samples were collected and preprocessed by fuzzy algorithm. The specific content of the evaluation index of 3D printing sample data is determined, and the evaluation system of 3D printing sample data is established. Fuzzy algorithm is used to determine the weight of data quality evaluation index of ceramic 3D printing samples. According to the steps of setting up the weight matrix of the evaluation index of 3D printed ceramic sample data, the weight matrix of the evaluation index is established. Because there are many data sources of ceramic 3D printing samples, there may be data conflicting with the description of the same object. Under the background of fuzzy algorithm, the effective dimension of ceramic 3D printing sample data is established. According to the membership function of each evaluation index, the fuzzy set of 3D printing sample data of ceramics is established. Finally, the data evaluation process is designed to realize the data evaluation of ceramic 3D printing samples based on fuzzy algorithm. The experimental results show that the evaluation accuracy of the method is higher than 96%, and the scores of users and experts on the interior design scheme are above 80 points.

Keywords

Fuzzy algorithm ceramic material 3D printing sample data data evaluation

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References

Cynthia

, Randles

and Sliva

M.D.

et al., The MERRA-2 aerosol reanalysis, 1980 – onward, part i: system description and data assimilation evaluation, Journal of Climate30(17) (2017), 6823.

Yang

, Yang

and Liu

et al., Research on 3D positioning of handheld terminal based on particle swarm optimization, Journal of Internet Technology20(2) (2019), 563–572.

, Shen

and Mi

W.J.

et al., Ship identification algorithm based on 3D point cloud for automated ship loaders, Journal of Coastal Research (2015), 28–34.

Asche

C.V.

, Seal

and Kahler

K.H.

, Evaluation of healthcare interventions and big data: review of associated data issues, Pharmacoeconomics35(7) (2017), 1–7.

Mitra

, Fatakdawala

and Nguyentruong

, Detection of pentosidine cross-links in cell-secreted decellularized matrices using time resolved fluorescence spectroscopy, Acs Biomater Sci Eng3(9) (2018), 1944–1954.

Parente

, Colannino

and Picconi

, Safety of oral alpha-lipoic acid treatment in pregnant women: a retrospective observational study, Eur Rev Med Pharmacol Sci21(18) (2017), 4219–4227.

Yang

, Jiang

and Ang

, A winner-take-all evaluation in data envelopment analysis, Annals of Operations Research278(2) (2019), 1–18.

Wimmer

, Tröscher

A.R.

and Brunner

, Systematic evaluation of RNA quality, microarray data reliability and pathway analysis in fresh, fresh frozen and formalin-fixed paraffin-embedded tissue samples, Sci Rep8(1) (2018), 6351.

Cuevas-Martinez

J.C.

and Yuste-Delgado

A.J.

, and a. trivino-cabrera, cluster head enhanced election type-2 fuzzy algorithm for wireless sensor Networks, IEEE Communications Letters (99) (2017), 1–1.

10.

Sinning

J.M.

, Trying to square the circle? integration of computed tomography data for the evaluation of mitral regurgitation, European Heart Journal38(28) (2017), 2227.

11.

Yang

and Hu

, Evaluation of retrieval methods of daytime convective boundary layer height based on lidar data, Journal of Geophysical Research-Atmospheres122(8) (2017), 4578–4593.

12.

Kaushal

and Khehra

B.S.

, BBBCO and fuzzy entropy based modified background subtraction algorithm for object detection in videos, Applied Intelligence47(4) (2017), 1–14.

13.

Richard

O.A.

, Jakhu

and Zomer

, Adherence and persistence among statin users aged 65 years and over: a systematic review and meta-analysis, J Gerontol A Biol Sci Med Sci73(6) (2017), 813–819.

14.

Gupta

, Mehlawat

M.K.

and Mahajan

, Data envelopment analysis based multi-objective optimization model for evaluation and selection of software components under optimal redundancy, Annals of Operations Research (1) (2018), 1–24.

15.

Kovalev

, Puchkov

N.V.

and Kovalev

S.S.

, The first quantitative evaluation data on vendian metamorphism in the eastern part of the bashkir meganticlinorium, Doklady Earth Sciences483(1) (2018), 1418–1422.

16.

Sevimoglu

, Turanli

and Bereketoglu

, Systems biomarkers in psoriasis: integrative evaluation of computational and experimental data at transcript and protein levels, Gene647 (2018), 157–163.

17.

Lokesha

, Shruti

and Deepika

, Reckoning of the Dissimilar Topological indices of Human Liver, Applied Mathematics & Nonlinear Sciences3(1) (2018), 265–276.

18.

Maximilian

and Hildegard

, Predicting the separation of time scales in a heteroclinic Network, Sciences4(1) (2019), 279–288.

19.

Dariusz

, Comparison of fractional order derivatives computational accuracy-right hand vs left hand definition, Sciences2(1) (2017), 237–248.

20.

Yan

and Li

F.J.

, Noether’s theorems of variable mass systems on time scales, Applied Mathematics & Nonlinear Sciences3(1) (2018), 229–240.

21.

Zou

and Liu

J.A.

, Mutual information-based two-phase memetic algorithm for large-scale fuzzy cognitive map learning, IEEE Transactions on Fuzzy Systems (99) (2017), 1–1.

22.

Cheng

and Fu

, Dielectric properties for differentiating normal and malignant thyroid tissues, Med Sci Monit24 (2018), 1276–1281.

23.

Xie

, Tu

and Xu

, Bacterial distributions and prognosis of bloodstream infections in patients with liver cirrhosis, Sci Rep7(1) (2017), 11482.

24.

Tan

Y.C.

and Zhang

et al., Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry, Annals of Operations Research248(1–2) (2017), 1–22.

Data evaluation method for ceramic 3D printing samples based on fuzzy algorithm

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