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Journals / Kastamonu Üniversitesi Orman Fakültesi Dergisi / 2019 / Cilt: 19 - Sayı: 3

Development of an Artificial Neural Network Model to Minimize Power Consumption in the Milling of Heat-Treated and Untreated Wood

Isıl İşlem Uygulanmış ve Uygulanmamış Odunun Frezelenmesinde Güç Tüketimini Azaltmak için Bir Yapay Sinir Ağı Modelinin Geliştirilmesi

Şükrü ÖZŞAHİN
Hilal SİNGER
Pages
317–328
DOI
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AI Summary & Key Takeaways

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

Furthermore, it was found that power consumption decreased with decreasing of feed rate and cutting depth.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim of study: The power consumption of machining operations is an important part of the total production cost.
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Key Findings & Data:Therefore, in this study, an artificial neural network (ANN) model was developed to model the effects of treatment, rotation speed, cutting depth, and feed rate on power consumption in the wood milling process.
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Conclusion & Impact:Furthermore, it was found that power consumption decreased with decreasing of feed rate and cutting depth.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Sinir Ağı · Frezeleme · Güç Tüketimi

Abstract

Aim of study: The power consumption of machining operations is an important part of the total production cost. Therefore, in this study, an artificial neural network (ANN) model was developed to model the effects of treatment, rotation speed, cutting depth, and feed rate on power consumption in the wood milling process. Material and methods: A multilayer feed-forward ANN was employed for the prediction of power consumption. The accuracy of the model was assessed by performance indicators such as MAPE, RMSE, and R². Main results: It has been observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values were obtained as 7.533, 0.027, and 0.9737 %, respectively, in the testing phase. Furthermore, it was found that power consumption decreased with decreasing of feed rate and cutting depth. Research highlights: The findings of this study can be used effectively in the forest industry to reduce the experimental time and costs.

Özet

Çalışmanın amacı: İşleme operasyonlarının güç tüketimi toplam üretim maliyetinin önemli bir parçasıdır. Bu nedenle, bu çalışmada odun frezeleme işleminde muamele, dönme hızı, kesme derinliği ve besleme hızının güç tüketimi üzerine olan etkilerini modellemek için bir yapay sinir ağı (YSA) modeli geliştirilmiştir. Materyal ve yöntem: İleri beslemeli çok katmanlı bir YSA güç tüketimini tahmin etmek için kullanılmıştır. Modelin doğruluğu, MAPE, RMSE ve R2 gibi performans göstergeleri aracılığıyla değerlendirilmiştir. Sonuçlar: YSA modelinin kabul edilebilir sapmalarla oldukça tatmin edici neticeler elde ettiği görülmüştür. MAPE, RMSE ve R2 değerleri, test aşamasında sırasıyla % 7.533, 0.027 ve 0.9737 olarak elde edilmiştir. Ayrıca, besleme hızının ve kesme derinliğinin azalması ile güç tüketiminin azaldığı bulunmuştur. Araştırma vurguları: Bu çalışmanın bulguları orman endüstrisinde deneysel zamanı ve maliyetleri azaltmak için etkili bir şekilde kullanılabilir.

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Cite This Article

ÖZŞAHİN, Ş., SİNGER, H. (2019). Development of an Artificial Neural Network Model to Minimize Power Consumption in the Milling of Heat-Treated and Untreated Wood. Kastamonu Üniversitesi Orman Fakültesi Dergisi, Cilt: 19 - Sayı: 3, 317–328. https://ajindex.com/articles/166829

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