Kulisz Monika, Zagórski Ireneusz, Józwik Jerzy, Korpysa Jarosław
Department of Organisation of Enterprise, Management Faculty, Lublin University of Technology, 20-618 Lublin, Poland.
Department of Production Engineering, Mechanical Engineering Faculty, Lublin University of Technology, 20-618 Lublin, Poland.
Materials (Basel). 2022 Jun 16;15(12):4277. doi: 10.3390/ma15124277.
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 and depth of cut a 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 and a 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.
本研究的主要目的是确定加工条件，以确保在使用PRECITOOL公司生产的高速钢钴合金整体双齿铣刀时，加工表面质量最佳、切削区切屑温度低且切屑具有良好的几何特征。作为研究对象，选择了由AZ91D合金制成的具有预定几何形状的样品。粗铣加工在DMU 65 MonoBlock立式铣削中心进行。通过确定诸如3D粗糙度参数、切屑温度、切屑形状和几何形状等可加工性指标，分析了AZ91D镁合金的可加工性。在大多数情况下，每齿进给量f和切削深度a参数的增加会导致3D表面粗糙度参数值的增加。增加所分析的加工参数并不会显著提高瞬时切屑温度。在当前切削条件下未观察到切屑着火现象。所进行的研究证明，在所采用的加工条件下，切屑温度未超过自燃温度。利用人工神经网络建模对加工变量工艺参数f和a与切削区温度T、3D表面的空间几何结构“Sa”和峰度“Sku”之间的因果关系进行了建模。在模拟过程中，使用了MLP和RBF网络、各种神经元激活函数和各种学习算法。基于学习和验证的质量以及学习和验证误差指标，对获得的建模结果进行了分析，并选择了最合适的网络。结果表明，在分析3D粗糙度参数（Sa和Sku）时，MLP网络取得了更好的结果；在分析最高温度时，RBF网络取得了更好的结果。
