Investigation of Welding Parameters of Arc Welding Electrodes by Artificial Intelligence Methods

Abstract

In recent years, many studies have been carried out on Artificial Immune System (AIS) with Clonal Selection Algorithm (CSA), which is one of the sub-methods of artificial intelligence, which is a very popular field. In this study, a hybrid model is proposed in which the AIS algorithm and fuzzy logic method are used together in order to find the most suitable values for welding machine electrodes, which have a wide usage area in the industry. In the study, a software has been developed that optimizes the welding parameters and gives their combinations in order to speed up the R&D activities carried out in order to make the welding consumable a better-quality product and to reduce the costs. With this study carried out in the Visual Studio environment, the yield strength (N/mm²) of welding consumables was optimized by using different welding input parameters. In this study, AIS clonal selection algorithm (CSA) and fuzzy logic hybrid algorithm method are discussed in order to speed up resource production R&D activities and reduce costs.

Keywords

• Artificial immune system
• Fuzzy logic
• Arc welding electrode
• Hybrid systems
• Clonal selection algorithm

Ark Kaynağı Elektrotlarındaki Kaynak Parametrelerinin Yapay Zekâ Yöntemleri ile İncelenmesi

Abstract

Son yıllarda oldukça popüler bir alan olan yapay zekânın alt yöntemlerinden Yapay Bağışıklık Sistemi (YBS) ile Klonal Seçim Algoritması (KSA) üzerine bir çok çalışma yapılmaktadır. Bu çalışmada, endüstride geniş bir kullanım alanına sahip kaynak makinası elektrotlarının en uygun değerlerinin bulunabilmesi için Yapay Bağışıklık Sistemi (YBS) algoritması ile, Bulanık Mantık yönteminin bir arada kullanıldığı hibrit bir model önerilmektedir. Gerçekleştirilen çalışmada kaynak sarf malzemesinin daha kaliteli bir ürün olması için gerçekleştirilen Ar-Ge faaliyetlerini hızlandırmak ve maliyetlerini düşürmek amacıyla kaynak parametrelerini optimize ederek kombinasyonlarını veren bir yazılım geliştirilmiştir. Visual Studio ortamında gerçekleştirilen bu çalışma ile farklı kaynak girdi parametreleri kullanılarak kaynak sarf malzemelerinin akma mukavemeti (N/mm²) optimize edilmiştir. Bu çalışmada kaynak üretim Ar-Ge faaliyetlerini hızlandırmak ve maliyetlerini düşürmek amacıyla YBS Klonal Seçim Algoritması (KSA) ile Bulanık Mantık hibrit algoritma yöntemi ele alınmıştır.

Keywords

• Yapay bağışıklık sistemi
• Bulanık mantık
• Ark kaynak elektrotu
• Hibrit sistemler
• Klonal seçim algoritması

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