Automating Daily Task in Manufacturing and Production Sites Via Machine Learning Intelligence - Rani, K. Swarupa; Kumarasamy, M.; Pattanaik, Balachandra; Jagannathan, Sharath Kumar; Pattnaik, Manjula; Girimurugan, B. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Robots have been utilized for some time now in the manufacturing industry. These robots work safely alongside humans and gain knowledge from their interactions with them. However, there are still sectors of the economy that are hesitant to adopt robots for a variety of reasons, including those pertaining to technology and the economy. The advancement of robotics has led to the creation of capabilities that span a greater breadth of applications than those that were previously utilized. In this paper, we develop an auto encoder based modelling to automate the daily task using robotic process automation in manufacturing and production sites. The unsupervised learning models achieves better processing of the automation and provides better accurate results than the existing methods. The software is able to perform simultaneous analysis on a number of logs, which enables it to discover processes and variations that were previously unknown. The platform has the potential to legitimately outperform technology-based businesses when it comes to the automation of processes, whether those processes are carried out in a physical or digital environment.