IndustriesVision AI Based Rivet & Fastener Inspection System
High-speed manufacturing environments depend on thousands of small mechanical fasteners whose failure can compromise product integrity, safety, and brand trust. In this case, missing or improperly fastened rivets were escaping manual inspection due to production speed, human fatigue, and visual inconsistency. Quality issues were often discovered only after products had shipped or failed in the field, leading to recalls, warranty costs, and reputational damage. Traditional machine vision solutions were either too rigid, too expensive, or too fragile for 24×7 industrial use.
RNDSquare engineered an edge-native Vision AI inspection system using industrial cameras and Nvidia Jetson-class processors mounted directly on the production line. The system captured synchronized high-resolution images, applied trained deep learning models to detect missing, misaligned, or defective rivets, and flagged anomalies in real time. The pipeline was designed for low-latency inference, high uptime, and operational robustness, with automatic health monitoring, fail-safe alerts, and remote update capabilities. The solution integrated directly into existing production control systems, ensuring minimal disruption to line operations.
This enabled true in-line quality control instead of post-production audits. Defects were detected immediately, scrap and rework were reduced, and traceability improved at the unit level. The manufacturer achieved higher first-pass yield, lower warranty risk, and a measurable improvement in production reliability, while quality teams shifted from firefighting to systematic process improvement.
40%
improvement over manual inspection
<2%
False positive rate after tuning
99%
System uptime
