There is a tremendous increase in the demand for converting biomaterials into high-quality industrially manufactured human body parts, also known as medical implants. Drug delivery systems, bone plates, screws, cranial, and dental devices are the popular examples of these implants - the potential alternatives for human life survival. However, the processing techniques of an engineered implant largely determine its preciseness, surface characteristics, and interactive ability with the adjacent tissue(s) in a particular biological environment. Moreover, the high cost-effective manufacturing of an implant under tight tolerances remains a challenge. In this regard, several subtractive or additive manufacturing techniques are employed to manufacture patient-specific implants, depending primarily on the required biocompatibility, bioactivity, surface integrity, and fatigue strength. The present paper reviews numerous non-degradable and degradable metallic implant biomaterials such as stainless steel (SS), titanium (Ti)-based, cobalt (Co)-based, nickel-titanium (NiTi), and magnesium (Mg)-based alloys, followed by their processing via traditional turning, drilling, and milling including the high-speed multi-axis CNC machining, and non-traditional  abrasive water jet machining (AWJM), laser beam machining (LBM), ultrasonic machining (USM), and electric discharge machining (EDM) types of subtractive manufacturing techniques. However, the review further funnels down its primary focus on Mg, NiTi, and Ti-based alloys on the basis of the increasing trend of their implant applications in the last decade due to some of their outstanding properties. In the recent years, the incorporation of cryogenic coolant-assisted traditional subtraction of biomaterials has gained researchers’ attention due to its sustainability, environment-friendly nature, performance, and superior biocompatible and functional outcomes fitting for medical applications. However, some of the latest studies reported that the medical implant manufacturing requirements could be more remarkably met using the non-traditional subtractive manufacturing approaches. Altogether, cryogenic  machining among the traditional routes and EDM among the non-traditional means along with their variants, were identified as some of the most effective subtractive manufacturing techniques for achieving the dimensionally accurate and biocompatible metallic medical implants with significantly modified surfaces.

The present work deals with drill wear monitoring using an artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of highspeed steel (HSS) drill bits for drilling holes on copper workpiece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feedrate, spindle speed, and drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data, and has been found to be satisfactory.

The bit error rate expression of the binary phase‐shift keying modulation scheme has been derived in a frequency selective fading channel for the fractional Fourier transform (FRFT) based orthogonal frequency‐division multiplexing (OFDM) system in the presence of carrier frequency offset (CFO). The performance of the FRFT based OFDM system has been found to be better than FFT‐based OFDM at different values of FRFT angle parameter ‘α’.

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Water is an important resource for life and its existence and, unfortunately, large quantities of water are being wasted on a daily basis. Monitoring the consumption of water can control water usage, and smart technologies can play a useful role. In this paper, a smart system based on Internet of Things (IoT) has been proposed to monitor the water consumption in an urban housing complex. An ultrasonic sensor, together with Arduino, continuously monitors the water level of water tanks on rooftops and sends these data to a server through a Wi-Fi module. Using the data collected from the IoT system, the daily and weekly average water requirement of households can be calculated. Support vector machines (SVM) are used to forecast water consumption. The observed readings are divided into training and testing datasets. Water consumption is predicted for each day for a user. Error is recorded as the difference between the actual consumption and the predicted value, and it decreases as the number of days increase. An algorithm to monitor leakage of water in the tanks has also been proposed. A web interface allows the user to visualize the water usage, monitor their consumption, and detect any leakage and leakage rate in the system.

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There exist numerous challenges during hole making in super alloys and other advanced materials such as the metal matrix composites. The conventional hole making operations such as drilling sometimes becomes uneconomical because of low productivity and poor quality. It becomes necessary to conceptualize and develop processes that are not only economical but also produce high-quality holes. Electric discharge drilling process is one such process that combines the effect of the electric discharge machining and the conventional drilling. In the present experimental investigation, experiments have been performed on a modified electric discharge machining setup with a facility to rotate the electrode. The work-piece material has been selected as Al6063/10% SiC metal matrix composite as tool wear is an important limitation during its conventional drilling. The major objective is to investigate the effect of the geometry of tool electrode on the process performance that has not been investigated exhaustively. The material removal rate has been selected as the measure of performance and criteria for comparison. It has been found that the geometry of the tool electrode influences the material removal rate and should be selected judiciously.