Abstract. The availability, adaptability, reliability and productivity of modern industrial complexes, maintenance costs, energy costs and industrial production costs depend also on the intensity of wear of the elements of tribo-mechanical systems. Gear cutting operations are usually a bottleneck of production, especially in higher types of production. Therefore, the necessity of researching the process of gear cutting by hob milling is obvious. In the paper a part of the results related to the possibility of direct measurement of the temperature in a very complex process of gear cutting of cylindrical gears by hob milling is given.
In this study, cutting tools average temperature was investigated by using thermal imaging camera of FLIR E50-type. The cubic boron nitride inserts with zero and negative rake angles were taken as cutting tools and round bar of EN 90MnCrV8 hardened steel was used as the workpiece. Since the life of the cutting tool material strongly depends upon cutting temperature, it is important to predict heat generation in the tool with intelligent techniques. This paper proposes a method for the identification of cutting parameters using neural network. The model for determining the cutting temperature of hard steel, was trained and tested by using the experimental data. The test results showed that the proposed neural network model can be used successfully for machinability data selection. The effect on the cutting temperature of machining parameters and their interactions in machining were analyzed in detail and presented in this study.
The objective of this study is to examine the influence of machining parameters on surface finish in turning difficult-to-cut-steel. A new approach in modeling surface roughness which uses design of experiments is described in this paper. The values of surface roughness predicted by different models are then compared. Adaptive-neuro-fuzzy-inference system (ANFIS) was used. The results showed that the proposed system can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments with central composition plan modeling technique can be effectively used for the prediction of the surface roughness for difficult-to-cut-steel.
In this study, cutting tool`s wear, temperature and forces during turning process were investigated. Used were two types of inserts HM and CBN were taken as cutting tools and round bar of EN 90MnCrV8 hardened steel was used as the workpiece. Since the life of the cutting tool material strongly depends upon cutting temperature, it is important to predict wear and heat generation in the tool. Determination of temperature field in tool was by thermal camera. Determined was dependence of temperature tool wear parameter for two cutting tool materials as well.
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