Abstract
BackgroundMachining using vertical CNC end mill is popular in the modern material removal industries because of its ability to remove the material at a fast rate with a reasonably good surface quality.MethodsIn this work, the influence of important common machining process variables like feed, cutting speed and axial depth of cut on the output parameters such as surface roughness and amplitude of tool vibration levels in Al-6061 workpieces has been studied. With the use of experimental result analysis and mathematical modelling, correlations between the cutting process conditions and process outputs are studied in detail. The cutting experiments are planned with response surface methodology (RSM) using Box-Behnken design (BBD).ResultsThis work proposes a multi-objective optimization approach based on genetic algorithms using experimental data so as to simultaneously minimize the tool vibration amplitudes and work-piece surface roughness. The optimum combination of process variable is further verified by the radial basis neural network model.ConclusionsFinally, based on the multi-objective optimization approach and neural network models an interactive platform is developed to obtain the correct combination of process parameters.
Highlights
Machining using vertical CNC end mill is popular in the modern material removal industries because of its ability to remove the material at a fast rate with a reasonably good surface quality
End milling operations are carried out with full percentage of radial immersion is conducted on the Al6061 work pieces
The data of the input and output factors were checked for normality using the probability plots as shown in the Fig. 4. It is observed from the normal probability plots of the amplitude and surface roughness, all the data points are distributed along the normal line which confirms that all the points are normally distributed
Summary
Machining using vertical CNC end mill is popular in the modern material removal industries because of its ability to remove the material at a fast rate with a reasonably good surface quality. For the metal-working industry, a continuous reduction in manufacturing cost is desirable. An important issue related to reduce overall cost consists of the removal of undesirable or excess work piece material during the machining process. Among different types of material removal operations, end-milling is most important common milling operation due to its capability of producing complex geometric surfaces with reasonable accuracy and surface finish. Over the last two decades, several works focused on the optimum selection of machining parameters based on various criteria such as using basic mathematical models. Brito et al (2014) developed a robust parameter design for the process parameters using the
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