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Machinability investigations on hardened AISI 4340 steel using coated carbide insert

ABSTRACT Hard turning AISI 4340 high strength low alloy steel Coated carbide inserts Machinability Design of experiments Response surface methodology The hard turning process with advanced cutting tool materials has several advantages over grinding such as short cycle time, process flexibility, compatible surface roughness, higher material removal rate and less en-vironment problems without the use of cutting fluid. However, the main concerns of hard turning are the cost of expensive tool materials and the effect of the process on machinability characteristics. The poor selection of the process parameters may cause excessive tool wear and increased work surface roughness. Hence, there is a need to study the machinability aspects in high-hardened components. In this work, an attempt has been made to analyze the influence of cutting speed, feed rate, depth of cut and machining time on machinability characteristics such as machining force, surface roughness and tool wear using response surface methodology (RSM) based second order mathematical models during turning of AISI 4340 high strength low alloy steel using coated carbide inserts. The experiments were planned as per full factorial design (FFD). From the para-metric analysis, it is revealed that, the combination of low feed rate, low depth of cut and low machining time with high cutting speed is beneficial for minimizing the machining force and surface roughness. On the other hand, the interaction plots suggest that employing lower cutting speed with lower feed rate can reduce tool wear. Chip morphology study indicates the formation of various types of chips operating under several cut-ting conditions.

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    ABSTRACT: In this study, predictive modelling was performed for the cutting forces generated during the orthogonal turning of AISI 316L stainless steel. An artificial neural network (ANN) and a multiple regression analysis were utilised. The input parameters of the ANN model were the cutting speed, feed rate and coating type. In the model, tungsten carbide cutting tools, uncoated and with two different coatings (TiCN ? Al2O3 ? TiN and Al2O3), were used. The ANN predictions closest to the experimental cutting forces were obtained for the main cutting force (Fc) and the feed force (Ff) by 3-7-1 and 3-6-1 network architectures with a single hidden layer, respectively. While the SCG learning algorithm provided the optimal results for Fc, the optimal results for Ff were provided by the LM learning algorithm. A very good performance of the neural network, in terms of agreement with the experimental data, was achieved. With the developed model, the cutting forces could be precisely predicted depending on the cutting speed, feed rate and coating type. The prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability.
    Neural Computing and Applications 10/2014; · 1.76 Impact Factor

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May 22, 2014