Superalloys have become increasingly used in the machining sector due to their high strength, temperature and machinability. One of these alloys, Nilo (Invar) 36, has a low thermal expansion and its use is rapidly increasing in areas where high temperature and expansion are not required, especially in composite mould applications, such as aerospace, electronics, measuring instruments and aerospace. In this study, a mathematical model based on artificial intelligence and an interactive visual interface in MATLAB software were developed according to the test results obtained from surface roughness Ra, cutting methods, rotational speeds, cooling method and cutting speed of Nilo 36 alloy. For the mathematical analysis of the measurements, the number of experiments to be performed by using Minitab program and Taguchi method was reduced to 32. The measurement results were modelled by Response Surface Design method and the factors affecting the surface roughness were determined in order of importance. A high-performance feed-forward artificial neural network has been developed using experimental data and an interactive interface has been prepared based on the developed model. Thus, the user can easily observe the cutting forces and surface roughness values for different cutting parameters with high accuracy.
cutting forces interface development machine learning Nilo 36 superalloy Response Surface Design
Marmara University
FEN-E 090517-0273
Experiments were carried out by using the experimental equipment taken Depertmant of Mechanical Engineering within the scope of FEN-E 090517-0273 project supported by BAPKO of Marmara University, Turkey.
FEN-E 090517-0273
Birincil Dil | İngilizce |
---|---|
Konular | Yapay Zeka, Mühendislik, Makine Mühendisliği |
Bölüm | Research Articles |
Yazarlar | |
Proje Numarası | FEN-E 090517-0273 |
Yayımlanma Tarihi | 15 Nisan 2021 |
Gönderilme Tarihi | 4 Ekim 2020 |
Kabul Tarihi | 28 Ocak 2021 |
Yayımlandığı Sayı | Yıl 2021 Cilt: 5 Sayı: 1 |