No abstract

This study has been conducted to optimize the dressing parameters to find the minimum surface roughness for internal grinding of hardened SKD11 steel using the Taguchi method. The input parameters used are coarse dressing depth, number of coarse dressing, fine dressing depth, number of fine dressing, non-feeding dressing, and dressing feed speed. The Analysis of Variance (ANOVA) and an analysis of the signal-to-noise (S/N) response are conducted to estimate the significance of each input parameter on the responses. It shows that the number of coarse dressing has the most decisive impact on Ra (88.28%). Furthermore, the discrepancy of the roughness average from the experiments and that from prediction are minor.

With the increasing wireless data traffic and data rate demands, milimeter-wave band corresponding to the frequency range 30-300 GHz has much attention of the researchers. Using multiple antennas at the transmitter and the receiver with beamforming helps overcome high transmission loss. Therefore, millimeter-wave Multiple-Input Multiple-Output (mmWave MIMO) is a key technology for the next generation wireless communication system. However, due to hardware constrains when operating in the high frequency range, the performance of a mmWave MIMO system will decrease. In this paper, we analysis system performance of a hybrid beamforming mmWave MIMO system with non-ideal hardware (phase noise and quantization noise).

This study presents the results of a study on multi-objective optimization of internal cylindrical grinding of hardened SKD11 steel in order to find one objective function which satisfy minimum surface roughness, Ra. By this procedure, an optimum set of dressing parameters such as coarse dressing depth, number of coarse dressing, fine dressing depth, number of fine dressing, non-feeding dressing, and dressing feed speed will be found. Taguchi design methodology is accepted to find the optimum set of dressing parameters which can lead a condition of objective function as above mentioned. ANOVA is conducted based on experimental results to find the significance of each input parameter on the responses. The results reveal that the optimum value of surface roughness is 0.111 μm when using the optimum dressing parameters such as fine dressing depth at level of 2, number of fine dressing at level of 3, number of non-feeding dressing at level of 4, number of coarse dressing at level of 3, coarse dressing depth at level of 2, and dressing feed rate at level of 1.