Dimensional Accuracy and Surface Quality of AZ91D Magnesium Alloy Components after Precision Milling

Capability Analysis of AZ91D Magnesium Alloy Precision Milling Process with Coated Tools

Process capability analysis is the main tool of statistical process control. It is used for the ongoing monitoring of product compliance with imposed requirements. The main objective and novelty of the study were to determine the capability indices for a precision milling process of AZ91D magnesium alloy. Machining was performed in terms of variable technological parameters and using end mills with protective TiAlN and TiB₂ coatings intended for the machining of light metal alloys. The Pp and Ppk process capability indices were determined based on the measurements of the dimensional accuracy of the shaped components that were taken on a machining centre with a workpiece touch probe. Obtained results demonstrated that the type of tool coating and variable machining conditions had a significant impact on the machining effect. The selection of appropriate machining conditions enabled a terrific level of capability to be achieved at a tolerance of 12 µm, several times lower than under unfavourable conditions where the tolerance was up to 120 µm. Improvements in process capability are mainly achieved by adjusting the cutting speed and feed per tooth. It was also shown that process estimation based on improperly selected capability indices might lead to an overestimation of the actual process capability.

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

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 f_z and depth of cut a_p 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 f_z and a_p 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 the Tool Cutting Edge Helix Angle on the Surface Roughness after Finish Milling of Magnesium Alloys

This paper shows the surface quality results after finishing milling of AZ91D and AZ31 magnesium alloys. The study was performed for variable technological parameters: cutting speed, feed per tooth, axial depth of cut and radial depth of cut. The tools used in the study were two carbide cutters with a different tool cutting edge helix angle. The measurement of the research results presented the surface roughness parameters was made on the lateral faces and the end faces of the specimens. Statistical analysis and simulations using artificial neural networks were carried out with the Statistica software. The normality of the distribution was examined, and the hypotheses of the equality of mean values and variance were verified. For the AZ91D magnesium alloy on the lateral and the end faces (Ra, Rz parameters), simulations were carried out. Two types of ANN were used: MLP (Multi-layered perceptron) and RBF (Radial Basis Function). To increase the machining stability and to obtain a high surface finish, the more suitable tool for finishing milling is the tool with a helix angle of λs = 20°. Artificial neural networks have been shown to be a good tool for predicting surface roughness parameters of magnesium alloys after finishing milling.