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

Abstract

The main purpose of the study was to define the machining conditions that ensure the best quality of the machined surface, low chip temperature in the cutting zone and favourable geometric features of chips when using monolithic two-teeth cutters made of HSS Co steel by PRECITOOL. As the subject of the research, samples with a predetermined geometry, made of AZ91D alloy, were selected. The rough milling process was performed on a DMU 65 MonoBlock vertical milling centre. The machinability of AZ91D magnesium alloy was analysed by determining machinability indices such as: 3D roughness parameters, chip temperature, chip shape and geometry. An increase in the feed per tooth fz and depth of cut ap parameters in most cases resulted in an increase in the values of the 3D surface roughness parameters. Increasing the analysed machining parameters did not significantly increase the instantaneous chip temperature. Chip ignition was not observed for the current cutting conditions. The conducted research proved that for the adopted conditions of machining, the chip temperature did not exceed the auto-ignition temperature. Modelling of cause-and-effect relationships between the variable technological parameters of machining fz and ap and the temperature in the cutting zone T, the spatial geometric structure of the 3D surface “Sa” and kurtosis “Sku” was performed with the use of artificial neural network modelling. During the simulation, MLP and RBF networks, various functions of neuron activation and various learning algorithms were used. The analysis of the obtained modelling results and the selection of the most appropriate network were performed on the basis of the quality of the learning and validation, as well as learning and validation error indices. It was shown that in the case of the analysed 3D roughness parameters (Sa and Sku), a better result was obtained for the MLP network, and in the case of maximum temperature, for the RBF network.

Influence of Cutting Parameters and Tool Geometry on Topography of Hard Turned Surfaces

“…A generalizable yet strong relationship cannot be observed for the Sku parameter based on the present experimental results. This result confirms the findings of [34].…”

“…Peng et al compared additively manufactured parts, upon which heat treatment was and was not applied, and found that the Ssk and Sku parameters are suitable for the characterization of the surfaces [33]. According to Kulis et al, who analyzed milled surfaces, the peak height (Sp), Ssk, and Sku parameters provide no clear information on the effects of the cutting parameters [34]. Karmiris-Obratanski et al carried out an in-depth analysis of the Ssk connected to surfaces machined by electro-discharge machining and found that Ssk is not significantly influenced by the machining parameters [35].…”

Influence of Innovative Post-Weld Finishing Method on Bead Surface Quality

“…In order to conduct a study on the innovative post-weld finishing effect in steel specimens produced using different welding methods and its impact on the key surface quality parameters, the 2D and 3D surface roughness measurements were performed [ 13 , 14 , 15 , 16 , 17 ]. The level of selected surface roughness parameters determined its functional properties: fatigue strength, wear and corrosion resistance, contact strength, tribological properties, adhesion, and others.…”

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

“…Analysis of variance (ANOVA) was performed to evaluate the arranged formula. Kulisz et al 18 measured machinability indices, for example, 3D roughness parameters, chip temperature, chip shape and geometry. Then analysed the machinability of AZ91D magnesium alloy.…”