Multi-Layered Leak Detection Approach

1. Predictive Leakage Detection
Using time-series data from sensors like pressure, temperature, fill level, and actuator force, AI4M’s models identify anomalies and drift patterns that indicate early signs of leakage. Especially effective in sachet, pouch, and bottling lines, this gives operators time to act before defects occur.

2. Visual Leak Detection
Our vision systems use high-speed cameras and AI-based detection to catch visible leaks—residual drips, pooling, or compromised seals—on fast-moving production lines in real time. Ideal for food, FMCG, and chemical industries.

3. AI-Based Causal Prediction & Suggestions
Beyond detecting leaks, our system identifies why they occurred. Using correlation modeling and causal inference across variables like sealing temperature, pressure, material type, and operator behavior, the system offers intelligent suggestions for root-cause resolution.

4. Autonomous Machine Setting Adjustment
When the cause and corrective steps are well-defined, the system can autonomously adjust parameters—like increasing seal pressure or modifying dwell time—through closed-loop control, reducing defect rates without human intervention.

5. Physics-Informed Neural Networks (PINNs)
In cases where direct measurement of leak potential isn’t possible (e.g., seal integrity during a thermal-pressure cycle), AI4M uses PINNs to simulate the internal physical behavior of materials and processes. This digital twin approach estimates leak likelihood even when no sensor or vision data is available.