An Intelligent Robotic Sorting and Predictive Maintenance System Using Machine Learning

The advancement of industrial automation necessitates intelligent robotic systems that are efficient in operation and proactive in maintenance. This work presents the development of an AI based robotic predictive maintenance and automated sorting system with integration of robotics, machine learning, IoT and mobile monitoring technologies. The smart sorting robot is designed and fabricated by 3D modeling and printing techniques, which is capable of automatically classifying objects by color and shape by Python and computer vision algorithms.To ensure reliable operation, various parameters including temperature, vibration, motion, motor current, and operating hours are continuously monitored using integrated sensors. The collected data is processed through machine learning models for anomaly detection and fault prediction, facilitating predictive maintenance and reducing unexpected downtime. An IoT-enabled architecture is implemented to transmit real-time data to a cloud platform, while a mobile application developed using MIT App Inventor provides live monitoring, maintenance scheduling, data analytics, and instant alert notifications through SMS and email. The proposed system enhances operational efficiency, improves equipment reliability, and supports Industry 4.0 applications in manufacturing, logistics, and smart warehousing environments.

Keywords—Robotic predictive maintenance, Automated sorting system, Artificial intelligence, Machine learning, Internet of Things (IoT), Computer vision, 3D printing, Fault detection, Condition monitoring, Real-time monitoring, Data analytics, Mobile application, Industrial automation, Smart warehousing, Industry 4.0.