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Journals / Selcuk Journal of Agriculture and Food Sciences / 2026 / Cilt: 39 Sayı: 1

Prediction of Time-series Friction Data using ANFIS

Cem Korkmaz(çukurova üniversitesi)0000-0003-1062-4581
İlyas Kacar(NİĞDE ÖMER HALİSDEMİR ÜNİVERSİTESİ)0000-0002-5887-8807
Pages
121–134
Publication Date
2025-03-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Background of the network used, its hyper-parameters, and the prediction performance are presented in the study.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Modelling is frequently used in science and industry.
📊
Key Findings & Data:The simulation takes 63 days, 18 hours and 27 minutes to calculate the real time of 60 seconds.
💡
Conclusion & Impact:Background of the network used, its hyper-parameters, and the prediction performance are presented in the study.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:İç yer fıstığı sınıflama · Ayrık eleman metodu · Uyarlanabilir nöro-bulanık çıkarım sistemi

Abstract

Modelling is frequently used in science and industry. Friction, wear, and corrosion issues are the main design criteria in peanut kernel grading machines. In this study, the time-series of friction force data is modelled with adaptive neuro-fuzzy inference system (ANFIS). Machine learning focuses on developing models for prediction and classification without explicit programming. The data on the friction force is obtained from a simulation based on the discrete element method. The simulation takes 63 days, 18 hours and 27 minutes to calculate the real time of 60 seconds. A Takagi-Sugeno type ANFIS network is constructed. The network is clustered using grid partitioning method. ANFIS helps to optimise machine performance by modelling friction data. In the obtained peanut kernel classification model, the correlation value is 0.799 and the root of the mean square error is 0.514 N. The percentage of the mean absolute error is found to be 1.666%. 100 iterations are run. Calculations take 20.7 seconds. The model has a high linear relationship. It is also observed that the ANFIS network eliminates the need for any pre-processing of the data. Background of the network used, its hyper-parameters, and the prediction performance are presented in the study.

Özet

Modelleme bilim ve endüstride sıklıkla kullanılmaktadır. Sürtünme, aşınma ve korozyon sorunları yer fıstığı sınıflandırma makinelerinde ana tasarım kriterleridir. Bu çalışmada, sürtünme kuvveti verilerinin zaman serisi uyarlanabilir nöro-bulanık çıkarım sistemi (ANFIS) ile modellenmiştir. Makine öğrenimi, açık programlama olmadan tahmin ve sınıflandırma için modeller geliştirmeye odaklanır. Sürtünme kuvveti verileri, ayrık eleman yöntemine (DEM) dayalı bir simülasyondan elde edilmiştir. Simülasyon, 60 saniyelik gerçek zamanı hesaplamak için 63 gün, 18 saat ve 27 dakika sürmektedir. Takagi-Sugeno tipi bir ANFIS ağı oluşturulmuştur. Ağ, ızgara bölümleme yöntemi kullanılarak kümelenmiştir. ANFIS, sürtünme verilerini modelleyerek makine performansını optimize etmeye yardımcı olur. Elde edilen yer fıstığı çekirdek sınıflandırma modelinde korelasyon değeri 0,798854 ve ortalama karesel hatanın kökü 0,51417 N'dir. Ortalama mutlak hatanın yüzdesi %1,6658 olarak bulunmuştur. 100 iterasyon çalıştırılmıştır. Hesaplamalar 20,70228 saniye sürmektedir. Model yüksek doğrusal ilişkiye sahiptir. ANFIS ağının verilerin herhangi bir ön işlemden geçirilmesi ihtiyacını ortadan kaldırdığı da gözlemlenmiştir. Çalışmada kullanılan ağın arka planı, hiper-parametreleri ve tahmin performansı sunulmuştur.

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Cite This Article

Korkmaz, C., Kacar, İ. (2025). Prediction of Time-series Friction Data using ANFIS. Selcuk Journal of Agriculture and Food Sciences, Cilt: 39 Sayı: 1, 121–134. https://ajindex.com/articles/1306688

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