As the market for AI-powered manufacturing continues to expand rapidly, manufacturers worldwide are accelerating their adoption of intelligent automation. The “AI in manufacturing” market is projected to grow dramatically from USD 23.4 billion in 2024 to USD 34.18 billion in 2025, with an expected CAGR of 35.3% from 2025 to 2030. Likewise, the “AI Vision Inspection” market is estimated to reach USD 31.6 billion by 2025, growing at a high CAGR of 22.5% through 2034.

In today’s industrial landscape, AI vision inspection systems capable of detecting microscopic defects in real time are emerging as the new standard. Traditional rule-based systems and manual inspection methods are limited in their ability to identify complex or irregular defects, resulting in the escape of faulty products that cause substantial financial losses and damage to brand reputation. The ability to monitor production quality continuously and respond instantly to issues has become a critical competitive advantage for manufacturers.

Challenge – Enhancing Quality Control in a Fully Automated Production Line

A global leader in advanced materials faced recurring quality issues in its automated production processes. Minute contaminants occasionally caused discoloration during the drying stage of white base materials, undermining product quality and consistency. Despite extensive efforts to strengthen quality competitiveness, the company encountered several challenges:

• Limitations and Inefficiency of Manual Inspection Relying on sample-based visual inspection after production made it difficult to detect defects early. When any defect was discovered, the company had to conduct a full reinspection of all finished products, consuming significant time and manpower.
• Lack of Data-Driven Quality Management Quality standards varied depending on each inspector’s subjective judgment, leading to inconsistencies and a lack of reliability in the inspection data. As a result, it was difficult to secure the objective data needed to trace the causes of defects or improve production processes.
• Slow Feedback and Reactive Response Defects were often identified only after production was completed, making immediate corrective action impossible when issues occurred. This reactive process led to large-scale losses and became a source of customer complaints.
• Dilemma of Full Automation Because the entire production process—from manufacturing to packaging—was fully automated, it was structurally difficult to add inspection steps in the middle of the process. Existing cameras were used only to confirm process progress and could not detect subtle discoloration.

Solution – Building a Real-Time Defect Detection System with Superb AI’s Complete AI Solution

To address the challenges, the company introduced Superb AI’s integrated solution to develop a real-time AI defect detection system. By leveraging Superb AI’s Vision Foundation Model “ZERO,” MLOps platform, and Edge AI technology, it successfully conducted a Proof of Concept (PoC) and deployed the system in its production environment.

The project was carried out in the following phases.

• High-Quality Data Collection and Curation High-resolution cameras and optimized lighting were installed on the production line to capture diverse types of defect data in real time. Through Superb Platform, the collected video data was automatically parsed into image units, while unnecessary or duplicate samples were efficiently filtered out and refined to ensure data quality.
• Accurate Labeling and Quality Management Using Superb Platform’s AI-assisted labeling function, both normal products and discolored defective regions were labeled quickly and precisely. Even when multiple labelers collaborated, the platform’s built-in quality control features ensured consistent inspection results, enabling the creation of a high-quality and reliable training dataset.
• Custom AI Model Development Based on the curated dataset, a high-performance defect detection model was rapidly developed on Superb Platform using a relatively small amount of data collected from the site. The platform enabled the full automation of the entire AI journey—from model training, evaluation, and deployment to continuous monitoring and performance optimization.
• Edge Deployment and Real-Time Analysis The developed AI model was deployed to edge computing devices through Superb Edge, enabling real-time analysis without routing data through cloud servers. This allowed the system to detect discoloration defects at the same speed as the production line and visualize results instantly on monitoring dashboards, ensuring seamless on-site inspection and response.

Benefit – Delivering Data-Driven Quality Innovation and Enhanced Productivity

After introducing the AI vision inspection system, the company achieved the following results:

• Significant Reduction in QA Time and Cost Manual reinspections previously performed after production were replaced by real-time automated inspection during production. This dramatically reduced inspection time and enabled personnel to be reallocated to higher-value tasks.
• Accurate and Consistent Data Quality The AI model achieved over 95% accuracy, detecting even the slightest discoloration that human eyes could easily overlook. This allowed the company to accumulate consistent, objective, and quantitative quality data for long-term process improvement.
• Real-Time Defect Detection and Proactive Response The system detected defects the moment they occurred on the production line, automatically sending alerts to enable rapid identification and resolution of root causes. This prevented large-scale losses from defective batches and significantly improved outgoing product quality.
• Data-Driven Process Improvement By analyzing accumulated data such as defect types, frequencies, and locations, the company identified which process stages were most prone to issues and established a solid foundation for continuous process optimization.

Scalability – The Next Stage of AI Adoption

The data and AI technologies established through this project can be expanded in multiple directions:

• Defect Classification and Root Cause Analysis Beyond simple defect detection, the system can automatically classify defect types and analyze recurring patterns to identify underlying causes.
• Integration with Automation Systems By connecting the AI model with automated equipment—such as robotic arms—the company can achieve fully autonomous, real-time quality inspection without human intervention.
• Expansion into an Enterprise-Wide Quality Management Platform The AI models developed for defect detection can be easily scaled and applied to other production lines or similar processes, enabling the creation of a unified, digital quality management platform across the organization.
• Predictive Maintenance and Yield Forecasting A robust predictive maintenance model requires the integration of various data sources—not only video but also temperature, vibration, current, and other sensor readings, along with operational logs—to accurately detect signs of errors. Superb Platform is designed to process and integrate not only vision data but also structured data from systems such as MES and ERP, as well as unstructured text data such as logs and reports. By analyzing these multimodal datasets, manufacturers can predict equipment failures in advance and forecast defect rates under specific conditions, ultimately identifying optimal production parameters for maximum yield.

Conclusion – AI Vision Inspection: A Core Strategy for Industrial Competitiveness

Accuracy and speed in quality management are key determinants of a manufacturer’s competitiveness. By partnering with Superb AI, the global manufacturer successfully implemented a real-time defect detection AI system, enabling data-driven quality innovation and enhanced productivity.

Superb AI’s integrated platform supports the entire AI lifecycle—from data preparation and model development to deployment and operation—helping enterprises overcome practical challenges and lower barriers to AI implementation in industrial environments. This success story clearly demonstrates how AI vision inspection can solve long-standing quality control challenges and create new value in manufacturing operations.