AI-powered Quality Control for Missile Interceptor Systems

A I • May 06,2024

A leading manufacturer of anti-rocket missile defense systems faced the challenge of ensuring the highest quality and precision in the assembly and inspection of critical components. They implemented a hybrid vision inspection system powered by deep learning and 3D imaging to address this issue. This AI-driven solution enabled them to achieve a 25% increase in quality within one year and a return on investment in two years.

Client:

A manufacturer of missile interceptor systems, at the forefront of aerospace engineering and defense technologies, develops systems capable of detecting, tracking, and neutralizing incoming missiles before they reach their targets.

Problem Statement:

● Human limitations: Manually inspecting hundreds of tiny components with critical tolerances is prone to errors and inconsistencies. Human inspectors can miss crucial defects like bent pins, missing parts, or faulty connections.

● Rigidity of traditional machine vision: Traditional systems rely on pre-defined rules that struggle to adapt to variations in lighting, component shapes, or manufacturing processes. This can lead to false positives (scrapping good parts) or false negatives (missing defects).

● Inability to analyze complex features: Traditional systems struggle to analyze features like glue application or solder points, which require assessing the amount or quality, not just presence or absence.

Results:

☑️ Faster ROI: The company achieved a 25% increase in quality within a year and a return on investment within two years.
☑️ Reduced Defects:Kitov.ai’s system identified errors in components that human inspectors missed. Preventing the inclusion of defective parts resulted in a considerable improvement in the final product’s quality.
☑️ Increased Inspection Speed: The AI system inspects each component in 30 seconds, compared to several minutes with manual inspection. This translates to a significant increase in inspection throughput.
☑️ Enhanced Detection Accuracy: Deep learning and 3D imaging in AI system provide superior accuracy in defect detection compared to traditional methods.
☑️ Data-driven Design: The AI system provided data on process engineering issues, allowing engineers to identify and redesign potential points of failure in the missile guidance system.

AI Solution:

Looking for a way to catch defects early in the production process, a leading manufacturer of anti-rocket missile defense systems turned to AI. Kitov.ai provided a hybrid visual inspection system that combines several advanced technologies:
● Deep Learning: This AI technique allows the system to identify and inspect objects like screws, surfaces, labels, and data ports with high accuracy. It can even be trained for entirely new products.
● 3D imaging: Multiple 2D images become the building blocks for a 3D model of the component within the system, enabling a more thorough inspection.
● Conventional Machine Vision: This established technology complements deep learning for specific tasks.
● Intelligent Robotic Planning: This uses algorithms to automate robot movement, illumination selection, and image capture during inspection.

Advantages of the Kitov.ai System:

● Superior Defect Detection: Deep learning and 3D imaging provide significantly higher accuracy in identifying defects compared to traditional vision systems.
● Flexibility: The solution can be easily adapted to inspect new products or components.
● Ease of Use: The software utilizes clear terms like “screw” and “label” instead of complex programming language, making it easier for non-experts to operate.

Implementation and Benefits

The company was able to integrate Kitov.ai’s system into their production line within a month. The system can inspect each component in just 30 seconds, significantly faster than manual inspection.
The AI system not only identified defects but also offered valuable insights.
● Process Improvement: Data from the system helped identify potential failure points in the missile guidance system design, allowing for improvements.
● Reduced Waste: Early detection of defects minimized the number of scrapped components.