This paper proposes a propelled home mechanization framework that utilizes an android application to control and screen the home apparatuses. This plan depends on the Internet of Things (IoT).In home computerization, every single home apparatus are organized together and worked without human intercession. In this framework, Raspberry Pi 4 is going to be interfaced with numerous sensors that may evaluate temperature and steaminess, light, energy, etc. Sensors were used to collect information and it would be stored in the data store and an example examination is done on the put away information which tells the client at which time the machines are typically on or off with the goal that they can be naturally controlled with no human intercession by watching the normal use design. The client moreover turns on/off remotely by means of mobile application and web-server.

Aluminum matrix composites (AMCs) are gaining increasing attention from various industries due to their lightweight and more excellent wear resistance than conventional materials. Manufacturers embracing that difficulty in machining MMC due to reinforcing particles abrasive nature shorten the tool life. Electro-discharge machining (EDM) is an enormously used non-conventional process to remove material in die making, aerospace, and automobile industries and machine any material with the highest hardness. Hence in the present study, EDM was performed on an aluminium alloy 8081 (AA8081) with reinforcement of 10% SiC, 5% B4C, and 5% Gr particles utilizing an ultrasonic cavitation assisted stir casting process. The machining investigation was carried out adopting face-centered central composite design (CCD) with three parameters such as current, pulse-on time, and pulse-off time to ascertain the effects of two sustainable measures, viz., Material removal rate (MRR) and tool wear rate (TWR) the data were collected. An Artificial Neural Network (ANN) model was developed based on data obtained from experiments. Finally, experimental values are compared with the predicted values of ANN and found high prediction accuracy. The advanced model results are used to approximate the responses fairly precisely. The version features a mean coefficient of correlation of 0.99072. Effects uncovered that the projected version is employed for the prediction of the complex EDM process.