The significance of the micro-geometries on the cutting edge is known from numerous studies conducted in the past. However, the effect of micro-geometry on the wiper facet (also called the wiper edge) is not known. Hence, this paper investigates the effect of different micro-geometries with a focus on geometry variation on the wiper edge of a milling insert on surface roughness and forces in face milling of SAE1070 high-carbon steel. Milling inserts with sharp, rounded, chamfered edges and their combinations were manufactured on the cutting edge and wiper edge for the study. Critical surface quality parameters such as the average surface roughness (Ra), mean depth of surface roughness (Rz), and force components such as radial force (Fx), cutting force (Fy), and axial force (Fz) were evaluated. Metal cutting tests were performed at three different cutting speeds and three different feed rates to study the influence of cutting parameters and the effect of edge geometries on surface roughness. The results were correlated with the force values to understand the machining dynamics. Finite element analysis was performed to evaluate the high and low-stress zones on the insert, workpiece, and chip to understand the metal cutting mechanism of different micro-geometries. The novel finding from the study is that having identical micro-geometries on the cutting and wiper edge is the preferred combination, whereas dissimilar micro-geometries result in reduced surface quality, increased forces, and high stress on the workpiece and chip.

Summary In recent times, evolution of communication technology and standard has grown in leaps and bounds from a conventional 1G communication technology towards the recent 5G and 6G technologies in a very short span of time. However, due to increasing scarcity of spectrum for these devices, cognitive radio networks (CRNs) have emerged to be promising solutions to allocate the required spectrum to the users in an intelligent manner. The method of compressive sensing‐based cyclo‐stationary feaure detection method is implemented based on a powerful CNN classifier to detect the presence or absence of PU activity. Detection probability and MSE have been improved, and the optimal detection has been reformulated for minimizing the possibility of error. Performance metrics have been compared against benchmark methods and superior performance reported. The sensing conduction and the accuracy of the proposed design are increased as 98.5%.

The spectrum scarcity problem in today’s wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels (R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models.

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