Application of an Adaptive Neuro-Fuzzy Inference System for the Optimal Analysis of Chemical-Mechanical Polishing Process Parameters

2001 ◽  
Vol 18 (1) ◽  
pp. 20-28 ◽  
Author(s):  
Z.-C. Lin ◽  
C.-Y. Liu
Keyword(s):  
Process Parameters ◽  
Fuzzy Inference System ◽  
Chemical Mechanical Polishing ◽  
Fuzzy Inference ◽  
Mechanical Polishing ◽  
Polishing Process ◽  
Inference System ◽  
Optimal Analysis ◽  
Neuro Fuzzy ◽  
Chemical Mechanical Polishing Process

Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

2019 ◽  
Vol 234 (5) ◽  
pp. 956-968
Author(s):  
Nripen Mondal ◽  
Madhab Chandra Mandal ◽  
Bishal Dey ◽  
Santanu Das
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Process Parameters ◽  
Predictive Models ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Inference System ◽  
Burr Height ◽  
Neuro Fuzzy

Burrs are undesirable materials beyond the work piece surface during drilling or other machining processes, thus this should be as less as possible during manufacturing. The experimental study has been conducted according to the full factorial design method. A total of 27 experiments were conducted by drilling on an Aluminum 6061T6 plate by choosing three factors and three levels of process parameters like drill diameter, point angle and spindle speed. In this research article, two predictive models, namely, adaptive neuro-fuzzy inference system and support vector regression, are developed using experimental data to estimate burr height and burr thickness. Then, these predictive models have been used to find out optimum process parameters for minimum burr height and burr thickness using genetic algorithm. It has been found that both the models are able to predict burr size and thickness with good accuracy, while the adaptive neuro-fuzzy inference system performs better than support vector regression.

scholarly journals A NOVEL METHOD FOR SELECTING THE OPTIMAL EDM PROCESS FOR HASTELLOY B2 USING THE MODIFIED-ADDITIVE RATIO ASSESSMENT METHOD (M-ARAS) BASED ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

2021 ◽  
Vol 55 (6) ◽  
Author(s):  
Ramasubbu Narasimmalu ◽  
Ramabalan Sundaresan
Keyword(s):  
Process Parameters ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Metal Removal ◽  
Optimization Problems ◽  
Assessment Method ◽  
Electrode Wear ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Edm Process

Electrode wear and metal removal exhibited nonlinear behavior in the Electrical Discharge Machining (EDM) of Hastelloy B2 plate. Hence, mathematical modeling was used to solve this problem. The hole size, pulse duration, duty cycle, and current were selected as inputs. Squareness and taper angle were considered as responses. Therefore, the Modified-Additive Ratio Assessment Method (M-ARAS) based Adaptive Neuro Fuzzy Inference System (ANFIS) method was used to find the optimum EDM process parameters. The overall analysis showed that the M-ARAS-based ANFIS algorithm provided a good fit for optimization of the process parameters and could be used for further multi-objective optimization problems.  

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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