Experimental and Numerical Study of Thermal Conditions in Magnesium Alloy Milling

MINIATURA 5

Funding Organization: National Science Centre

Project title: Experimental and Numerical Study of Thermal Conditions in Magnesium Alloy Milling

Agreement number: DEC-2021/05/X/ST8/00159

Project implementation period: 01.10.2021 - 30.09.2022

Principal Investigator: dr inż. Monika Kulisz

Project value: 45 692,00 PLN

Funds granted for Lublin University of Technology: 45 692,00 PLN

Abstract: The aim of the scientific activity is to determine the influence of technological parameters of the milling process and the geometry of the tool tip on the maximum chip temperature in the cutting zone. The increase in cutting speed during high-speed machining may be characterized by a decrease in the values of the cutting force components as well as a decrease in temperature in the cutting zone. The process parameters on which the final effect of the process will depend are, among others: technological parameters (cutting speed vc, feed per tooth fz, depth of cut ap) and tool blade geometry (different rake angles γ). The test will be carried out on the AVIA VMC 800HS machining center, for which the maximum spindle speed is n = 24,000 rpm, and the maximum speed of the feed movement is 40 m / min. The treatment will be carried out ""dry"". Two magnesium alloys, a casting grade - AZ91D, and a grade intended for plastic forming - AZ31 - will be used as the material. For machining, cutters with a diameter of d = 16mm, with different rake angles γ = 5 ° and γ = 30 ° will be used (tools made to special order). The workpiece during processing will be analyzed with the FLIR SC6000HS high-speed thermal imaging camera to determine the temperature in the cutting zone.
In addition, the enormous, still not fully used possibilities of modeling the dynamics of processing are provided by the advanced technique of artificial neural networks. Its use may facilitate the selection of the most suitable technological parameters, ensuring the optimization of the milling process (increase in efficiency) and maintaining conditions that do not cause chip ignition. The results of experimental tests - the value of the signal from the thermal imaging camera - will be the basis for modeling the temperature in the cutting zone. The input layer of the network for the milling process will be variable technological parameters such as vc, fz and ap and the rake angle γ, while the output layer - the temperature of the chips in the cutting zone. These simulations with the use of artificial neural networks will be carried out using the Statistica Neural Networks software. The following networks will be used for modeling: RBF (Radial Basis Function) and MLP (Multi-Layered Perceptron). As a result of the simulations, it will be possible to develop process models, which will allow for forecasting the maximum temperature in the cutting zone, when the machining parameters are known.
The project will be implemented in the discipline of mechanical engineering. A measurable and documented effect of the action taken will be the future publication of the effects of the action in journals important for the discipline of mechanical engineering. Additionally, it is assumed that the effects of the project will be disseminated at scientific conferences, where they will be disseminated among people interested in the subject of the project.