scholarly journals Adaptive Neuro-Fuzzy Inference System for Modelling the Effect of Slurry Impacts on PLA Material Processed by FDM

Polymers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 118
Author(s):  
Bahaa Saleh ◽  
Ibrahem Maher ◽  
Yasser Abdelrhman ◽  
Mahmoud Heshmat ◽  
Osama Abdelaal
Keyword(s):  
Weight Gain ◽  
Fused Deposition Modeling ◽  
Synergetic Effect ◽  
Solid Particles ◽  
Percentage Error ◽  
Inference System ◽  
Average Percentage ◽  
Fused Deposition ◽  
Effect Of Water ◽  
Neuro Fuzzy

In this research, the effect of water-silica slurry impacts on polylactic acid (PLA) processed by fused deposition modeling (FDM) is examined under different conditions with the assistance of an adaptive neuro-fuzzy interference system (ANFIS). Building orientation, layer thickness, and slurry impact angle are considered as the controllable variables. Weight gain resulting from water, net weight gain, and total weight gain are the predicting variables. Results uncover the accomplishment of the ANFIS model to appropriately appraise slurry erosion in correlation with comparing real data. Both experimental and ANFIS results are almost identical with average percentage error less than 5.45 × 10−6. We observed during the slurry impacts tests that all specimens showed an increase in their weights. This weight gain was finally interpreted to the synergetic effect of water absorption and the solid particles fragmentations immersed within the specimens due to the successive slurry impacts.

Development and Characterizations of a Projection Stereolithography

2017 ◽  
Vol 751 ◽  
pp. 160-166
Author(s):  
Pornsak Srisungsitthisunti ◽  
Thossaporn Kaewwichit
Keyword(s):  
3D Printing ◽  
Layer Thickness ◽  
Fused Deposition Modeling ◽  
3D Structure ◽  
Percentage Error ◽  
3D Printer ◽  
Average Percentage ◽  
Fused Deposition ◽  
Custom Made ◽  
Projection Stereolithography

Stereolithography is a manufacturing process capable of building a truly high resolution 3D structure by solidifying the liquid monomer in a layer by layer fashion. Currently, there are many developments toward new 3D printing techniques leading to needs for methods of characterization to improve printing process for higher performance. In this study, we propose to create a bottom-up projection stereolithography to accommodate a 3D printing technique. Our system was designed for combining with a regular fused deposition modeling (FDM) process for multi-material application. In addition, we developed a method for characterizations different specifications of a custom-made projection stereolithography. Our 3D printer can create an object up to 25 mm x 25 mm x 15 mm of length, width and height, respectively. We minimized the layer thickness error by modifying a screw and spring components in order to precisely control the movement of the vertical stepping. The light source distance and the calibration factor were also importance factors to obtain the better precision of finished parts. Based on the proposed characterization method, the 3D printer was able to achieve the lateral resolution of 0.05 mm and a vertical step resolution of 0.01 mm. The average percentage error of built part were 0.32 % on X-axis and 0.25 % on Y-axis laterally and 0.60 % error on the layer thickness.

scholarly journals Prediksi Hasil Ujian Kompetensi Mahasiswa Program Profesi Dokter (UKMPPD) dengan Pendekatan ANFIS

2018 ◽  
Vol 2 (2) ◽  
pp. 554-559
Author(s):  
Fajri Marindra Siregar ◽  
Gunadi Widi Nurcahyo ◽  
Sarjon Defit
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Average Percentage ◽  
Neuro Fuzzy ◽  
Average Percentage Error

Tujuan utama dari penelitian ini adalah untuk memprediksi hasil ujian kompetensi mahasiswa program profesi dokter (UKMPPD) menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS). Data didapatkan dari database mahasiswa Fakultas Kedokteran Universitas Riau tahun 2015 yang berjumlah 170 data. Variabel input yang digunakan meliputi status kepesertaan, lama studi, dan Indeks Prestasi Kumulatif. Selanjutnya data dianalisis menggunakan software MATLAB dengan pengaturan jumlah membership  function 2 2 2 dan type membership funtion gbell. Hasil penelitian menunjukkan bahwa metode ANFIS mampu memprediksi hasil UKMPPD dengan nilai Mean Average Percentage Error (MAPE) sebesar 0,07%, minimal 0,00% dan maximal 0,44%.

Penerapan Metode ANFIS untuk Prediksi Nilai Akhir dan Kelulusan Uji Kompetensi

2018 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Rayendra
Keyword(s):  
Word Processing ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Information And Communication ◽  
Anfis Method ◽  
Final Score ◽  
Computer Literate ◽  
Competence Test

To improve the graduation of Computer Literate Certified Professional (CLCP) competence test conducted by Competence Test of Information and Communication Technology (TUK-TIK) needs to be done continuous improvement by increasing try out competency test. Past values of the competency test can be used as modeling to predict the final score and the passing of the competency test. With the modeling can be predicted the passing of competency test participants through try out-try out done so that can be known weakness of candidate competency test from three units of CLCP competence. The modeling used to predict the final score and the passing of this competency test is the Adaptive Neuro Fuzzy Inference System (ANFIS) method. Used 20 past data of competency test participants with 6 criteria as input value from three CLCP competence units namely Word Processing, Spreadsheet, and Presentation. The resulting prediction is accurate enough with MAPE (Mean Absolute Percentage Error) value for each competency unit of 0.31492%, 0.284202%, and 0.267167%

scholarly journals Kajian Adaptive Neuro-Fuzzy Inference System (ANFIS) Dalam Memprediksi Penerimaan Mahasiswa Baru Pada Universitas Buana Perjuangan Karawang

2021 ◽  
Vol 6 (1) ◽  
pp. 44-54
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

Abstract - The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang.   Keywords: ANFIS, Backpropagation, Hybrid, Prediction

Mapping arsenic vulnerability at different spatial scales using statistical and machine learning models 

2021 ◽  
Author(s):  
Sonal Bindal
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Fuzzy Inference ◽  
Spatial Scales ◽  
Fuzzy Model ◽  
Fuzzy Regression ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Predictive Algorithms

<p>In the recent years, prediction modelling techniques have been widely used for modelling groundwater arsenic contamination. Determining the accuracy, performance and suitability of these different algorithms such as univariate regression (UR), fuzzy model, adaptive fuzzy regression (AFR), logistic regression (LR), adaptive neuro-fuzzy inference system (ANFIS), and hybrid random forest (HRF) models still remains a challenging task. The spatial data which are available at different scales with different cell sizes. In the current study we have tried to optimize the spatial resolution for best performance of the model selecting the best spatial resolution by testing various predictive algorithms. The model’s performance was evaluated based of the values of determination coefficient (R<sup>2</sup>), mean absolute percentage error (MAPE) and root mean square error (RMSE). The outcomes of the study indicate that using 100m × 100m spatial resolution gives best performance in most of the models. The results also state HRF model performs the best than the commonly used ANFIS and LR models.</p>

scholarly journals Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

2015 ◽  
Vol 8 (1) ◽  
pp. 369-384 ◽  
Author(s):  
K. Ramesh ◽  
A. P. Kesarkar ◽  
J. Bhate ◽  
M. Venkat Ratnam ◽  
A. Jayaraman
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Microwave Radiometer ◽  
Percentage Error ◽  
Computational Techniques ◽  
Temperature Profiles ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Temperature And Humidity ◽  
Rainy Days

Abstract. The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

APLIKASI SISTEM CERDAS UNTUK PREDIKSI ENERGI LISTRIK PEMAKAIAN SENDIRI DI PT INDONESIA POWER SUB UNIT PLTA KABUPATEN WONOGIRI

2017 ◽  
Vol 8 (2) ◽  
pp. 489
Author(s):  
Herliyani Hasanah ◽  
Nurmalitasari Nurmalitasari
Keyword(s):  
Fuzzy Inference System ◽  
Input Data ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy Input

Kebutuhan akan energi listrik menjadi kebutuhan primer nasional. Dalam keberlangsungan proses produksi energi listrik pada pembangkitan – pembangkitan diperlukan energi listrik untuk pemakaian sendiri. Dalam penelitian ini dibangun sebuah aplikasi sistem cerdas untuk memprediksi energi listrik pemakaian sendiri di PT Indonesia Power sub unit PLTA Wonogiri. Pada penelitian ini menggunakan 2 kelompok input, yaitu input FIS (Fuzzy Inference System) dan input pada NN (Neuro Fuzzy). Input data  merupakan data produksi harian energi listrik di PLTA Wonogiri selama kurun waktu 2010 – 2016. Variabel data yang digunakan dalam penelitian ini adalah data produksi listrik untuk pemakaian PLTA Wonogiri adalah energi listrik yang dihasilkan PLTA Wonogiri dengan satuan KwH (f), elevasi muka air waduk dengan satuan meter (a1) dan debit air yang masuk ke turbin dengan satuan /detik (a2).  Output yang diperoleh adalah pusat centroid (m), derajat keanggotaan (mf), bobot (w) dan konsekuen parameter ( c ). Dari hasil pengujian diperoleh keluaran dengan performansi yang optimal pada saat Fuzzy C Means 2 kelas dengan parameter laju pembelajaran 0.4, momentum 0.6 dengan bessar Mean Percentage Error 0.377970875.  Kata kunci:  prediksi, pemakaian sendiri, energi listrik, fuzzy inference system, neuro fuzzy

scholarly journals Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

2018 ◽  
Author(s):  
Naser Mahdiabadi ◽  
Gholamreza Khanlari
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Modulus Of Elasticity ◽  
Fuzzy Inference ◽  
Rock Slope Stability ◽  
Percentage Error ◽  
Inference System ◽  
Meaningful Relationship ◽  
Neuro Fuzzy

The uniaxial compressive strength (UCS) and modulus of elasticity (E) are two important rock geomechanical parameters that are widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. In the present study, prediction of UCS and E was investigated in calcareous mudstones of Aghajari Formation using multiple linear regression (MLR), multiple nonlinear regression (MNLR), artificial neural networks (ANN), and adaptive neuro-fuzzy ınference system (ANFIS). For this purpose, 80 samples from calcareous mudstones were subjected to the point loading, block punch, and cylinder punch tests. The performance of developed models was assessed based on determination coefficients (R2), mean absolute percentage error (MAPE), and variance accounted for (VAF) indices. The comparison of the obtained results revealed that, among the studied methods, ANFIS is the most suitable one for predicting UCS and E. Moreover, the results showed that ANN and MLNR respectively predict UCS and E better than MLR and a meaningful relationship between the observed and estimated UCS values in all regressions.

Modelling of Grewia mollis Stem Bark Gum Extraction Yield Using Neuro-Fuzzy Technique

2018 ◽  
Vol 34 ◽  
pp. 70-80 ◽  
Author(s):  
Emmanuel Olusola Oke ◽  
Oladayo Adeyi ◽  
Abiola John Adeyi ◽  
Kayode Feyisetan Adekunle
Keyword(s):  
Fuzzy Inference ◽  
Water System ◽  
Stem Bark ◽  
Extraction Process ◽  
Extraction Yield ◽  
Percentage Error ◽  
Process Time ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to model and predict Grewia Polysaccharide Gum (GPG) extraction yield from Grewia mollis (GM) powder/water system. The data for modelling the process behaviour consisted of four inputs (process temperature, GM powder/water ratio, process time and pH) and GPG yield as the output. The gbell Membership Function (MF) was used for the fuzzification of input variables and hybrid algorithm was chosen for the learning method of input–output data of the process. Simulation study was conducted on the developed ANFIS architecture at different MFs and epoch numbers to establish minimum error and maximum correlation coefficient (R) of the model. From the results obtained, ANFIS can be used as a reliable tool for modelling and prediction of GPG powder/water extraction process behaviour. The R between the experimental and predicted values was found to be high (> 0.96) and the mean percentage error was less than 2%, showing the great efficiency and reliability of the developed model.

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