Using a segmented grinding wheel improves chip release and heat release condition out of the cutting zone, facilitates the introduction of coolant into the cutting zone, as well as increases the ability to clean the grinding wheel surface. Therefore, using segmented grinding wheel promises to improve the quality and efficiency of the machining process. This paper presents a study about the multi-objective optimization of grinding process using a segmented grinding wheel. The parameters that are shosen as the output criteria are the surface roughness, amplitudes of system vibrations in X, Y, Z directions (Ax, Ay, Az), and the material removal rate (MRR). The experiments were performed in the surface grinding machine with the workpiece material of DIN 1.2379 steel and the grinding wheel material of aluminum oxide. The experimental matrix was designed using Taguchi method with nine experiment (orthogonal array L9) and with four input parameters (number of grooves, workpiece velocity, feed rate, and depth of cut). From the experimental data, the influence of input parameters on the output parameters were investigated. The TOPSIS method was applied to solve the multi-objective optimization problem. Then, the optimized set of input parameters was determined to ensure the minimum value of surface roughess, minimum values of three vibarion components, and to ensure the maximum value of MRR. Finally, the future research directions of this study were also proposed.
The surface texture on the EDM is an important quality indicator since it directly affects the cost of the further finishing work. The coating over the tool electrode in EDM can improve productivity, electrode wear resistance and surface quality. In the present study, the surface roughness of the EDM machined surface with coated and uncoated electrodes was evaluated. Al and AlCrNi coated Al electrode has been used for the study on machining Titanium alloy (Ti-6Al-4V). Current (I), voltage (Vg) and pulse on time (Ton) have been used as technology parameters under Taguchi method with regression model and optimal technology parameters. It was found as I and Vg are the parameters could strongly affect the surface quality. The coated tool electrode can produce better surface quality than uncoated tool electrode. The optimal technological parameters with coated and uncoated electrodes were found as I = 10 A, Ton = 500 µs and Vg = 40 V.
This article presents empirical study results when milling SCM440 steel. The cutting insert to be used was a TiN coated cutting insert with tool tip radius of 0.5 mm. Experimental process was carried out with 18 experiments according to Box-Behnken matrix, in which cutting speed, feed rate and cutting depth were selected as the input parameters of each experiment. In addition, cutting force was selected as the output parameter. Analysis of experimental results has determined the influence of the input parameters as well as the interaction between them on the output parameters. From the experimental results, a regression model showing the relationship between cutting force and input parameters was built. Box-Cox and Johnson data transformations were applied to construct two other models of cutting force. These three regression models were used to predict cutting force and compare with experimental results. Using parameters including coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)) and percentage mean absolute error (% MAE) between the results predicted by the models and the experimental results are the criteria to compare the accuracy of the cutting force models. The results have determined that the two models using two data transformations have higher accuracy than model not using two data transformations. A comparison of the model using the Box-Cox transformation and the model using the Johnson transformation was made with a t-test. The results confirmed that these two models have equal accuracy. Finally, the development direction for the next study is mentioned in this article
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