Background

As one of the world’s leading technology manufacturers, the customer operates large-scale production lines for components such as fingerprint recognition modules, CTC connectors, and semiconductors. With thousands of parts processed daily, even a small inspection error could lead to major quality issues, production delays, or customer claims.

The manufacturer needed a smarter, faster, and more scalable approach to surface defect detection—especially for materials with complex shapes, reflective surfaces, and micro-level abnormalities that were difficult to catch with traditional rule-based inspection systems.

By partnering with AIMatics, the company implemented a deep learning-based AI Visual Inspection System across multiple production lines. This system delivered real-time, high-accuracy defect detection with minimal human intervention, significantly improving efficiency, traceability, and production consistency.

Challenge

The production process involved components with reflective materials, uneven textures, and soldered microstructures—making it difficult for human inspectors or conventional vision systems to reliably detect fine scratches, contaminations, or soldering defects. Moreover, frequent changes in inspection standards and tight takt times demanded a solution that could adapt quickly without compromising speed or accuracy.

Solution

The manufacturer adopted AIMatics’ AI Visual Inspection Solution, featuring:

• Supervised Anomaly Detection to identify irregular shapes, patterns, and defects

• A multi-step labeling and learning process, reducing development time and improving inspection accuracy

• Grade-based control system allowing quick reconfiguration of acceptance criteria

• Automatic learning system (AATS) for continuous model updates without manual engineering

This flexible system was applied to high-complexity parts like fingerprint modules and CTC connectors, achieving consistent results in various production environments.

Results

After deploying AIMatics' AI Visual Inspection System across its production lines, the manufacturer achieved substantial improvements in both quality assurance and operational efficiency.

The system consistently delivered a 100% defect detection rate, even for components with highly reflective surfaces, complex geometries, and micro-level abnormalities. The false reject rate dropped to just 3.1%, outperforming the company's internal QC threshold of 5%, and helping to reduce unnecessary rework and material waste.

Beyond accuracy, the system significantly streamlined on-site operations. By reducing inspection takt time to as low as 2.75 seconds per part, the company was able to maintain high-throughput manufacturing without compromising on precision.

As a result, over 30 workers per line were reallocated or replaced, translating into a monthly cost savings of more than $22,000 USD per line.

For the customer, this wasn’t just a quality improvement—it was a strategic shift toward intelligent automation, enabling scalable, self-learning inspection that evolves with the complexity of modern manufacturing.