scholarly journals Fuzzy inference system for modelling machining parameters in electrical discharge machining

2018 ◽  
Vol 400 ◽  
pp. 042049
Author(s):  
S Rebai ◽  
A Belloufi ◽  
M Abdelkrim ◽  
I. Rezgui ◽  
I Baci ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Electrical Discharge ◽  
Fuzzy Inference ◽  
Electrical Discharge Machining ◽  
Machining Parameters ◽  
Inference System

scholarly journals Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 922 ◽  
Author(s):  
C. J. Luis Pérez
Keyword(s):  
Fuzzy Inference System ◽  
Electrical Discharge ◽  
Material Removal ◽  
Fuzzy Inference ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Operating Conditions ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Inference System

Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.

scholarly journals Fuzzy inference system based prediction of electrical discharge machining quality

Tehnika ◽  
2021 ◽  
Vol 76 (3) ◽  
pp. 318-325
Author(s):  
Marin Gostimirović ◽  
Dragan Rodić ◽  
Milenko Sekulić
Keyword(s):  
Fuzzy Inference System ◽  
Electrical Discharge ◽  
Fuzzy Inference ◽  
Electrical Discharge Machining ◽  
Inference System ◽  
Machining Quality ◽  
Machined Parts ◽  
Effective Selection ◽  
Input Variables ◽  
Edm Process

Quality and productivity are two most important performances of electrical discharge machining (EDM). This paper presents the application of a fuzzy inference system (FIS) for prediction of machining quality in the EDM process. Specifically, the FIS conducted modeling of geometrical accuracy and surface finish of EDM machined parts. With the fuzzy inference system model, the input variables are discharge current and pulse duration, while the output parameters are gap distance between the electrodes and surface roughness of the workpiece. The performance of the proposed FIS provides a more effective selection of the EDM input values, which leads to better machining conditions and quality of the final product. The fuzzy inference system based modeling of the EDM process showed a very good agreement compared to the experimental data.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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