Efficiency, speed, and precision are essential in modern manufacturing. AI Maglev Conveyor system, combining magnetic levitation (maglev) technology with artificial intelligence (AI), revolutionizes automated production processes. This system reduces maintenance costs and downtime by eliminating friction, enhancing operational efficiency. It transports goods swiftly with minimal energy consumption, optimizing resource use and supporting sustainability. AI integration enables real-time monitoring and adaptive control, allowing businesses to respond to production demand fluctuations and streamline supply chain operations. The AI Maglev Conveyor offers smooth, silent operation, accommodating diverse product types and sizes for flexible manufacturing without extensive reconfiguration. AI algorithms optimize routing, reduce cycle times, and improve throughput, creating an agile production line adaptable to market changes. This applied research paper introduces the Maglev Conveyor system, featuring an electromagnetic controller and multiple movers to enhance automation. It offers cost savings as an alternative to setups using six-axis robots or linear motors, with precise adjustments for robotic arm loading. Operating at high speeds minimizes treatment time for delicate components while maintaining precision. Its adaptable design accommodates various materials, facilitating integration of processing stations alongside electronic product assembly. Positioned between linear-axis and robotic systems in cost, the Maglev Conveyor is ideal for flat parts requiring minimal travel, transforming production efficiency across industries. It explores its technical advantages, flexibility, cost reductions, and overall benefits.

Production Lines and Conveying Systems are the staple of modern manufacturing processes. Manufacturing efficiency is directly related to the efficiency of the means of production and conveying. Modelling in the industrial context has always been a challenge due to the complexity that comes along with modern manufacturing standards. Long Short-Term Memory is a pattern recognition Recurrent Neural Network, that is utilised on a simple pneumatic conveying system which transports a wooden block around the system. Recurrent Neural Networks (RNNs) capture temporal dependencies through feedback loops, while Long Short-Term Memory (LSTM) networks enhance this capability by using gated mechanisms to effectively learn long-term dependencies. Conveying systems, representing a major component of production lines, are chosen as the target to model to present an approach applicable in large scale production lines in a simpler format. In this paper data from sensors are used to train the LSTM in order to output an Automaton that models the conveying system. The automaton obtained from the proposed LSTM approach is compared with the automaton obtained from OTALA. The resultant LSTM automaton proves to be a more accurate representation of the conveying system, unlike the one obtained from OTALA.