Automating Daily Task in Manufacturing and Production Sites Via Machine Learning Intelligence

Authors

  • K. Swarupa Rani Assistant Professor, Department of IT, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India.
  • M. Kumarasamy Assistant Professor, Department of Computer Science, College of Engineering and Technology, Wallaga University, Nekemte, Ethiopia, Africa
  • Balachandra Pattanaik Professor, Department of Electrical and computer engineering, College of Engineering and Technology, Wallaga University, Nekemte, Ethiopia, Africa.
  • Sharath Kumar Jagannathan Assistant Professor, Frank J. Guarini School of Business, Saint Peter's University, Jersey City, NJ-07306, USA.
  • Manjula Pattnaik Associate Professor, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, KSA.
  • B. Girimurugan Assistant Professor, KL Business School, Koneru Lakshmaiah Education foundation (Deemed to be University), Andhra Pradesh, India.

Keywords:

Task, Manufacturing, Production Sites, Machine Learning, Artificial Intelligence

Abstract

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.

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Published

05.12.2023

How to Cite

Rani, K. S. ., Kumarasamy, M. ., Pattanaik, B. ., Jagannathan, S. K. ., Pattnaik, M. ., & Girimurugan, B. . (2023). Automating Daily Task in Manufacturing and Production Sites Via Machine Learning Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 47–56. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4027

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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