OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

Modeling and optimization of flank wear and surface roughness of Monel-400 during hot turning using artificial intelligence techniques

This work aims to model and investigate the effect of cutting speed, feed rate, depth of cut and the workpiece temperature on surface roughness and flank wear (responses) of Monel-400 during turning operation. It also aims to optimize the machining parameters of the above operation. A power-law model is developed for this purpose and is corroborated by comparing the results with the artificial neural network (ANN) model. Based on the coefficient of determination (R2), mean square error (MSE), and mean absolute percentage error (MAPE) the results of the power-law model are found to be in close agreement with that of ANN. Also, the proposed power law and ANN models for surface roughness and flank wear are in close agreement with the experiment results. For the power-law model R2, MSE, and MAPE were found to be 99.83%, 9.9×10-4, and 3.32×10-2, and that of ANN were found to be 99.91%, 5.4×10-4, and 5.96×10-2, respectively for surface roughness and flank wear. An error of 0.0642% (minimum) and 8.7346% (maximum) for surface roughness and 0.0261% (minimum) and 4.6073% (maximum) for flank wear were recorded between the observed and experimental results, respectively. In order to optimize the objective functions obtained from power-law models of the surface roughness and flank wear, GA (genetic algorithm) was used to determine the optimal values of the operating parameters and objective functions thereof. The optimal value of 2.1973 µm and 0.256 mm were found for surface roughness and flank wear, respectively.

HoT Turning Operation in Finite Element Analysis

There is a requirement for materials of high hardness and protection from cutting. As we probably aware the machining of these materials has dependably been an incredible test. Machining of these composites and materials required for cutting high-quality, which now and again isn't prudent and in some cases even illogical. Also, even the non-ordinary procedures are by and large constrained to the perspective of efficiency. The benefits of simple part assembling of exorbitant hard materials can be considerable as far as decreasing expenses and lead times machined contrasted with the customary one includes the warmth treatment, granulating and manual completing/cleaning. In the hot working at a temperature of work piece is expanded in order to decrease its shear quality. This paper will centre around hot working of high manganese steel with oil fuel. A few parameters, for example, cutting pace, feed, profundity of cut and the temperature of the work piece are taken. An investigation was led. Indeed, even the machining process was reproduced in ANSYS and Disfigure 2D to discover relating distortion, rate of hardware wear, cutting power and the temperature dissemination.