In precision manufacturing, particularly within the automotive and aerospace industries, quality control is critical to ensuring the safety, reliability, and longevity of engine components.
Key Challenges
Precision meets production pressure
The client, a high-tech manufacturing company, needed to:
Improve the precision of inspections for critical engine components
Detect micro-defects such as scratches, pores, and micro-cracks
Replace manual mirror-based inspection techniques that were time-consuming and error-prone
Integrate a real-time, automated quality control system to support fast-paced CNC machining workflows
Key components delivered
Intelligent inner surface inspection system
Seargin engineered a next-generation vision system designed to automate the inspection of internal cylindrical engine components, ensuring higher defect detection rates while reducing cycle times.
- Robotic arm with vision sensors
- • A robotic arm was integrated into the production line.
- • Equipped with a dedicated industrial-grade camera and custom lighting array.
- • Designed specifically for capturing high-resolution images of internal surfaces.
- Machine learning-powered defect detection
- • Developed a custom machine learning model trained to recognize CNC machining patterns.
- • Algorithmic detection of anomalies, including: micro-cracks, surface pores, scratches and material inconsistencies.
- • Continuous learning from new datasets to improve detection accuracy over time.
- Real-time inspection efficiency
- • Entire scanning and analysis process completed in under 30 seconds.
- • Automatically flagged components with defects for rework or rejection.
- • Seamlessly integrated into the client’s existing production and QA infrastructure.
Business results
Higher accuracy, lower inspection Times, greater efficiency
Enhanced defect detection
The automated system achieved significantly higher accuracy in detecting internal imperfections compared to manual methods.
Reduced inspection time
Inspection time dropped to under 30 seconds per component, freeing up valuable production line resources and improving workflow efficiency.
Boosted production efficiency
By eliminating bottlenecks in quality control, the client achieved a marked increase in output capacity and reduced error-related downtime.
