Abstract
A promising paradigm in contemporary manufacturing is the fusion of Artificial Intelligence (AI) technology with Flexible Manufacturing Systems (FMS). FMS, characterized by their ability to adapt to dynamic production demands, have found a perfect ally in AI, which offers advanced capabilities in data analysis, decision-making, and process optimization. This abstract provides an overview of the synergistic relationship between AI and FMS and highlights the potential benefits and challenges associated with their integration. Firstly, this abstract explores the role of AI in FMS, focusing on three key areas: planning and scheduling, intelligent control, and predictive maintenance. FMS is equipped with AI technologies like machine learning and deep learning to quickly analyze massive amounts of data, spot trends, and make precise predictions. These capabilities enhance production planning by optimizing resource allocation, reducing setup time, and minimizing production downtime. Additionally, intelligent control systems powered by AI enable real-time adjustments in response to changing conditions, leading to improved system flexibility, agility, and responsiveness. Due to a number of strong arguments, the combination of Flexible Manufacturing Systems (FMS) with Artificial Intelligence (AI) is of great research significance. The research significance of combining AI with Flexible Manufacturing Systems lies in the potential to significantly enhance operational efficiency, adaptability, and decision-making capabilities in manufacturing. This integration enables manufacturers to optimize resource utilization, mitigate downtime, and proactively manage maintenance, ultimately leading to improved productivity, cost savings, and competitiveness. By addressing the challenges and exploring the opportunities offered by AI in FMS, researchers can contribute to the advancement and transformation of the manufacturing industry. Due to the abundance of possibilities offered on the global market, conflicting situations can develop while choosing a certain motorcycle. There may be many alternatives to the initial choice or there may not always be a fixed amount of possibilities available. The possibility of not having an acceptable option for the criterion exists as well. “Multiple Criteria Decision Making” is a technique designed for the optimization of problems with an “infinite or finite number of choices” and the MCDM technique. “WSM method” is used to optimize the process in this paper.
In artificial intelligence with flexible manufacturing system evaluated six criteria and got the values. in that values .FMS 1 has got the first rank, FMS 2 got the second rank,FMS 3 got the third rank and FMS 4 got the last rank.In conclusion, the integration of AI with Flexible Manufacturing Systems offers numerous opportunities for enhanced operational efficiency, productivity, and adaptability.