Feature: AI
Figure 1: CNC turning machine that can make measurements with a vibration sensor and a dynamometer
Training neural networks for machining applications
By Orhan Güngör, Burdur Mehmet Akif Ersoy University, and Abdülkadir Çakir, Associate Professor, Isparta Uygulamalı Bilimler University, both in Turkey
M
achining is one of the most important techniques in modern-day manufacturing, and it continually evolves. One vital raw material category in machining is the nickel-based super-alloy. More recently, nickel-based super-alloys
have become more widespread, especially in the aviation sector, industrial gas turbines, space applications, engines, nuclear reactors, submarines, steam production facilities, petrochemical devices, heat-resistant applications, and many more. Each of these sectors uses machining tools, which are subject
32 September/October 2020
www.electronicsworld.co.uk
to many forces that lead to excessive vibrations which can cause deterioration and damage as well as affect processing stability and quality. To develop a stable processing strategy, it is necessary to have tool and machine vibration models to analyse the forces at play and identify the right solutions to mitigate those negative forces or avoid them entirely.
Relying on studies In the machine industry, tool wear and surface roughness are important in determining cutting conditions. Studies have shown that determining the exact best values for, say material and tool lubrication, under various cutting conditions enables a more economical and efficient cutting process. To reduce its speed and the effects of surface roughness on the
cutting tool, the cutting process is performed under high pressure. One of the reasons for applying pressure is to reduce fractures on the cutting edge and the workpiece, and hence reduce vibrations. Increasing the pressure of the cooling water allows faster cutting speeds. Predicting dimensional changes in cutting tools during the
cutting process is especially important for understanding the machine’s functionality and the lifespan of the tools. Variables such as cutting speed, depth and feed rate play an important role in predicting vibrations. Anova variance analysis testing can also be used to determine the effects of various cutting parameters. Increased vibrations on the cutting tool are indicative of an
increase in surface roughness. It is possible to say that during the cutting process of aluminium, light steel and PVC materials, cutting depth has minimum effect on tool vibration, whilst cutting force has maximum effect. Te system used for determining cutting force and vibration
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