The machining of hard materials with the most economical process is a challenge that is the aim of production systems. Increasing demands of the market require a hard processing hardened steel in order to avoid finishing grinding. This research considers the turning of hardened steel without cooling with two types of tools: cubic boron nitride (CBN) and hard metal (HM) inserts. To estimate the influence of machining conditions on cutting temperature, a central composition design with three factors on five levels was used. The development of advanced models allows one to meet the accelerated demands in terms of productivity, product quality, and reduced production costs. Based on experimental data, three input regimes (cutting speed, feed, and depth of cut), and one attributive factor (tool material) were used as input variables, while cutting temperature was used as the output of the adaptive neuro-fuzzy inference systems (ANFIS). The model was trained, tested, and validated with a combined input/output data set. The obtained ANFIS model could be applied with high precision to determine the cutting temperature in machining of hardened steel. From an economic point of view, the obtained model can directly affect the cost of processing because cutting temperature and tool life are directly relieved.

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.

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.

No abstract