E2Packman

VeNIT Lab brings cutting-edge expertise in AI-driven simulation, reliability modeling, and real-time analytics to the E2PACKMAN project. Our team participates in developing digital twins, predictive maintenance tools, and anomaly detection systems tailored for advanced electronic packaging processes. MarUn focuses on AI-driven anomaly detection, failure prediction, and failure categorization, leveraging collected data to assess reliability trends and support early detection of packaging-related performance degradation. Leveraging experience in real-time signal processing, IoT-enabled manufacturing, and data-driven optimization, we help integrate smart, resilient, and efficient workflows into packaging production lines. Through this work, we aim to enhance innovation capacity, improve process reliability, enable early fault detection, and accelerate the adoption of intelligent manufacturing solutions across the European electronics packaging sector.

• Development of AI-driven models for anomaly detection, fault prediction, and defect categorization in electronic packaging processes.
• Creation of digital twin frameworks to simulate, optimize, and validate packaging process performance and reliability.
• Generation and use of synthetic data to address the scarcity of rare failure cases for model training and validation.
• Implementation of real-time signal processing and data analytics for early fault detection and process optimization.
• Application of data-driven methodologies to monitor reliability trends, do process benchmarking and assess long-term performance degradation.
• Integration of intelligent algorithms and analytics tools into manufacturing workflows to enhance quality control and production efficiency.
• Collaboration with industrial partners to ensure practical applicability, scalability, and technology transfer of developed solutions.