Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm

Abstract

Magnesium alloys are advanced, light materials used widely in industries and milling is one of the material removal processes that are extensively used. In this present study, the experimental work has been carried out based on a Box-Behnken design by mainly considering three factors, i.e., cutting speed, feed, and depth of cut. The first part of in this study, the effects of Response Surface methodology (RSM) and Artificial Neural Network (ANN) models were evaluated and compared. The RSM and ANN models provide the average error of 2.40 % and 1.52 %, respectively, it recommends that ANN is a more efficient methodology for the prediction of the optimal output response than RSM. The predicted model has been coupled with evolutionary optimization technique genetic algorithm (GA) to determine the optimum cutting parameters to attain the minimal surface roughness with respect to the wide ranges of machining parameters and GA suggest the surface roughness of 1.0926 μm with the optimized machining parameters. The evaluation of these observations proves that the proposed methods are capable of determining the optimum machining parameters for modern materials. Keywords: artificial neural network, genetic algorithm, magnesium alloy, milling, optimization Zlitine na osnovi magnezija (Mg) so napreden lahek material, ki se pogosto uporablja v razli~nih vejah industrije. Njegova mehanska obdelava z rezkanjem je ena od najpogostej{ih metod odstranjevanja materiala za dokon~no oblikovanje razli~nih industrijskih izdelkov. V predstavljeni {tudiji avtorji opisujejo eksperimentalno delo izvedeno na osnovi Box-Behnkenovega dizajna z upo{tevanjem treh faktorjev: rezalne hitrosti, pomika in globine reza. V prvem delu te {tudije so avtorji ovrednotili in primerjali u~inke dveh uporabljenih modelov: metodologije reakcije povr{ine (RSM; angl.: Response Surface Methodology) in umetne nevronske mre`e (ANN, angl: Artificial Neural Network). RSM in ANN modela sta predvidela povpre~no 2,40 % oz. 1,52 % napako. To pomeni, da je ANN u~inkovitej{a methodologija za napoved optimalne (reakcije) storilnosti. Model za prognozo so avtorji zdru`ili z evolucijsko optimizacijsko tehniko genetskih algoritmov (GA) in na ta na~in dolo~ili optimalne rezalne parametre za dosego minimalne povr{inske hrapavosti v obmo~ju dokaj {iroko izbranih parametrov mehanske obdelave. GA napoveduje povr{insko hrapavost 1.0926 μm pri optimiziranih parametrih mehanske obdelave. Ovrednotenje teh opazovanj je potrdilo da, je s predlaganimi metodami mo`no dolo~iti optimalne parametre mehanske obdelave modernih materialov. Klju~ne besede: umetna nevronska mre`a, genetski algoritem, zlitina na osnovi Mg, rezkanje, optimizacija

Research, Modelling and Prediction of the Influence of Technological Parameters on the Selected 3D Roughness Parameters, as Well as Temperature, Shape and Geometry of Chips in Milling AZ91D Alloy

“…By analysing the data presented in Table 1, it can be seen that in the case of the milling process, the tests were carried out for various materials, such as aluminium alloys [39,43,[45][46][47]50,53,54], magnesium alloys [55,56], titanium alloys [37,40,57], cobalt alloys [44], nickel alloys [48,[58][59][60] and steel [41,42]. Additionally, it can be stated that as far as the milling roughness parameters are concerned, only 2D parameters are modelled, mainly the Ra parameter.…”

“…It seems that such a narrow scope of research is insufficient to carry out a detailed analysis of surface conditions. In the case of temperature modelling, research works were carried out on materials such as aluminium or nickel alloys [43,53,54,59].…”

High Speed Machining for Enhancing the AZ91 Magnesium Alloy Surface Characteristics Influence and Optimisation of Machining Parameters

“…µm at condition (v=40 m/min, f=0.1 mm/rev, a=0.5 mm) and in the studies using milling operation 9,[13][14][15][16]18,23,[33][34][35][36][37] , the lowest roughness value reported is 0.061 µm at condition (v = 900 m/min, f = 0.03 mm/z, a = 0.2 mm). In the present study, the lowest Ra value (0.067 µm) is observed at the combination of the highest speed and lowest feed condition (Exp.…”

PTFE in Wet and Dry Drilling: Two-Tier Modeling and Optimization through ANFIS

“…Davim et al [20] studied the drilling of glass fibre reinforced plastics (GFRP). e recent research studies [21][22][23][24][25][26][27][28] also built RSM models for optimization. e use of uncoded coefficient that is obtained from experimental data may not be accurate in modeling.…”