Advanced Robotics and AI in Manufacturing: Enhancing Efficiency and Precision

The main purpose of this research work is to manage the workflow in a cloud environment by utilising the Manufacturing Sector (MFS) model along with the integration of a Deep Learning technique known as the Long Short Term Memory (LSTM) algorithm. The LSTM helps to utilize a cloud computing environment to regulate the forecast of future workloads and to supply the resources that are required to consistently maintain a balanced workflow. The input data collection rates collected by robotics devices serve as the foundation for all projections. The data that is input is either stored or processed by virtual machines, which are located in a cloud environment. It is possible that the robotics devices that were utilised in the testing were the same or different. When compared to other deep learning and MFS schedulers, the results of the simulation demonstrate that the MFS-LSTM model is significantly more effective than anything else. The findings indicate that the MFS-LSTM is capable of achieving superior processing capabilities while simultaneously spending less energy and reacting to tasks in a more expedient manner.