Abstract: The main innovation of Industry 4.0, which involves human-robot cooperation, is transforming industrial operation facilities. Robotic systems have been developed as modern industrial solutions to assist operators in carrying out manual tasks in cyber-physical industrial environments. These robots integrate unique human talents with the capabilities of intelligent machinery. Due to the increasing demand for modern robotics, numerous ongoing industrial robotics studies exist. Robots offer advantages over humans in various aspects, as they can operate continuously. Enhanced efficiency is achieved through reduced processing time and increased industrial adaptability. When deploying interactive robotics, emphasis should be placed on optimal design and improvisation requirements. Robotic design is a very challenging procedure that involves extensive development and modeling efforts. Significant progress has been made in robotic design in recent years, providing multiple approaches to address this issue. Considering this, we propose utilizing the Backpropagation Autoregressive Integrated Moving Average (BP-ARIMA) combination model for designing and improving a novel industrial robot mechanism. The design outcomes were evaluated based on performance indicators, including accuracy, optimal performance, error rate, implementation cost, and energy consumption. The evaluation findings demonstrate that the suggested BP-ARIMA model offers optimal design for industrial robotics.

Yuanyuan Liu. “Design and Improvement of New Industrial Robot Mechanism Based on Innovative BP-ARIMA Combined Model”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.8 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140870

@article{Liu2023,
title = {Design and Improvement of New Industrial Robot Mechanism Based on Innovative BP-ARIMA Combined Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140870},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140870},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Yuanyuan Liu}
}