Physics Maths Engineering
Artificial Intelligence Techniques for Sustainable Reconfigurable Manufacturing Systems: An AI-Powered Decision-Making Application Using Large Language Models
Hamed Gholami
Peer Reviewed
Related Subjects
Abstract
Abstract
Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. The findings reveal that machine learning and big data analytics, as well as fuzzy logic and programming, stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems.
Key Questions
1. What AI techniques are most effective for sustainable reconfigurable manufacturing systems?
The study identifies machine learning, big data analytics, and fuzzy logic as the most promising AI techniques for enhancing sustainability in reconfigurable manufacturing systems.
2. How can large language models aid decision-making in manufacturing systems?
Large language models can process and analyze vast amounts of data, providing insights and recommendations that support decision-making processes in manufacturing systems.
3. What role does fuzzy logic play in reconfigurable manufacturing systems?
Fuzzy logic helps in handling uncertainties and imprecise information, enabling more flexible and adaptive control mechanisms in reconfigurable manufacturing systems.
4. Why is it important to evaluate AI techniques for manufacturing systems?
Evaluating AI techniques allows for the identification of the most effective methods to improve efficiency, adaptability, and sustainability in manufacturing systems.
5. What is the role of AI in manufacturing?
AI in manufacturing enhances efficiency, quality, and flexibility by enabling predictive maintenance, optimizing production processes, and facilitating intelligent decision-making.
6. How does machine learning improve manufacturing processes?
Machine learning improves manufacturing by analyzing data to predict equipment failures, optimize production schedules, and enhance product quality, leading to increased operational efficiency.
