Challenge

In industrial environments with high production volumes, traditional quality control processes are not always sufficient to detect defects in time. The company needed a system capable of improving traceability, reducing unidentifiable errors and decreasing downtime associated with quality incidents, all without slowing down the production line or overloading the technical staff.

Solution

To address this need, an autonomous defect detection system was designed and implemented based on advanced artificial intelligence algorithms and neural networks.

The solution uses audio recordings made in the final stages of quality control, converting sound patterns into data that the model analyses to identify possible anomalies in real-time.

The system not only detects the exact moment a deviation occurs but also generates immediate alerts and corrective recommendations so that the plant teams can act without delays.

Additionally, its modular architecture allows integration with existing production and management systems, centralising all information into a single intuitive visual interface.

Thanks to the continuous learning capability of its neural models, the system improves its accuracy with each iteration, adapting to new scenarios and types of parts without the need for manual reprogramming.

In parallel, it incorporates analytical panels that provide management with a global overview of the quality status of production, which facilitates the planning of resources, workstations, and staff shifts more efficiently. In short, it is a solution that automates inspection, reduces reliance on manual control, and turns data into a strategic tool for optimizing quality and operational performance.