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Journals / Ege Akademik Bakış / 2018 / Cilt: 18 - Sayı: 2

Improvement of Manufacturing Processes by Artificial Neural Networks Analysis

Yapay Sinir Ağları Analizi İle İmalat Süreçlerinin İyileştirilmesi

Salih AKA
Gökhan AKYÜZ
Pages
261–271
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

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

The results show that artificial neural network models can be well adapted to manufacturing processes.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Manufacturing processes consist of activities affected by a large number of variables.
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Key Findings & Data:In the study, a manufacturing process with 15 input variables was modeled using artificial neural networks, network training was provided, and a trained network was used to obtain the best output performance in the current situation.
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Conclusion & Impact:The results show that artificial neural network models can be well adapted to manufacturing processes.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Manufacturing processes consist of activities affected by a large number of variables. The aim of this study is to show that improvements can be made by using artificial neural network methods at stages of manufacturing such as planning of processes, forecasting of the future situation, monitoring and control. In the study, a manufacturing process with 15 input variables was modeled using artificial neural networks, network training was provided, and a trained network was used to obtain the best output performance in the current situation. Artificial neural networks are useful tools in finding out the consequences of any change that may occur in variables and in improving the processes with this way. The results show that artificial neural network models can be well adapted to manufacturing processes.

Özet

İmalat süreçleri çok sayıda değişken tarafındanetkilenen faaliyetlerden oluşmaktadır. Buçalışmanın amacı imalat süreçlerinin planlanması,gelecek durumunun tahmin edilmesi, takipve kontrolü gibi aşamalarda yapay sinir ağlarıyöntemi kullanılarak iyileştirmeler yapılabileceğinigöstermektir. Çalışmada, 15 girdi değişkeninesahip bir imalat süreci, yapay sinir ağları yöntemiile modellenmiş, ağın eğitilmesi sağlanmış vemevcut durumda en iyi çıktı performansı alabilmekiçin eğitilen ağ kullanılmıştır. Yapay sinir ağları,değişkenlerde meydana gelebilecek herhangi birdeğişim karşısında ortaya çıkabilecek sonuçlarıntespitinin sağlanmasında ve bu yolla süreçleriniyileştirilmesinde faydalı bir araçtır. Sonuçlar, yapaysinir ağı modellerinin imalat süreçlerine iyi adapteolabildiğini göstermektedir.

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Cite This Article

AKA, S., AKYÜZ, G. (2022). Improvement of Manufacturing Processes by Artificial Neural Networks Analysis. Ege Akademik Bakış, Cilt: 18 - Sayı: 2, 261–271. https://ajindex.com/articles/495189

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