AI Summary & Key Takeaways
AI InsightThus, the user can easily observe the cutting forces and surface roughness values for different cutting parameters with high accuracy.
📌 3 Key Takeaways
Abstract
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.