Pakistan Science Abstracts
Article details & metrics
No Detail Found!!
A dynamic learning decision-making method for achieving industry 5.0 objectives as case study in the bioengineering and biomedical engineering industry of pakistan
Author(s):
1. Tariq Javid: Department of Biomedical Engineering, Faculty of Engineering Sciences and Technology, Hamdard University, Pakistan
2. Saghir Ahmed Sheikh: Department of Applied Sciences, Faculty of Engineering Sciences and Technology, Hamdard University, Pakistan
Abstract:
The evolution of industrial levels, from 1.0 to 5.0, has been driven by advancements in steam-powered systems, electric motor systems, computer technology, cyber-physical systems, and collaborative robots. The research presents a method to achieve Industry 5.0 objectives in the bioengineering and biomedical engineering industry of Pakistan. The proposed approach combines artificial intelligence techniques and movable electro-mechanical systems to create collaborative robots. These robots, acting as intelligent agents, assist human experts in complex tasks and contribute to a production learning environment. The aim was to investigate a hybrid approach that integrates Industry 4.0 and Industry 5.0 concepts, developing a dynamic learning decision-making process model. The model ranks actions in a human-like manner, resulting in a digital cognitive clone that mirrors human decision-making behavior. By employing this method, collaborative robots perform tasks more efficiently, approaching the capabilities of human experts. In the food industry context, the proposed method supports growers in specific tasks while considering product preservation constraints. This research contributes to the advancement of Industry 5.0 objectives by showcasing the integration of human expertise and artificial intelligence in achieving human-centric goals.
Page(s): 201-201
DOI: DOI not available
Published: Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023
Keywords:
digital twin , humancentric solutions , collaborative robots , Bioengineering , industry 50
References:
References are not available for this document.
Citations
Citations are not available for this document.
0

Citations

0

Downloads

236

Views