Edge Industrial Product Quality Inspection to Facilitate Production Line Automation
Hardware: reComputer J1020v2 with NVIDIA Jetson Nano
Application: Quality Inspection
Industry: Industry Automation
Deployment Location: Poland
Leveraging the integration of computer vision design, Jetson hardware components, and advanced image recognition algorithms, the system achieves swift and precise detection of product appearance quality. This enables the efficient quantitative storage of comprehensive product-quality data samples.
Through the closed-loop correlation and analysis of product quality inspection data alongside manufacturing process data, a holistic examination of factors influencing delivered product quality is conducted. This inclusive approach facilitates accurate prediction of cracking defects, enabling timely alarms for potential production line issues and supporting informed decision-making responses. This integrated system ensures not only efficient quality control but also proactive measures for enhanced manufacturing processes.
Challenge
Quality inspection often involves a range of manual tasks, including visual confirmation, to verify components’ accuracy in terms of color, shape, texture, and positioning. This proves challenging due to significant product variations. Inspectors must continually adapt to diverse quality requirements, leading to inaccuracies and inconsistent results. As production volume increases, inspectors meet health issues like eye fatigue from prolonged, repetitive inspections, allowing more defects to pass through and functional components to be rejected. Manual inspection is constrained by rising costs, training time, and limited resources, making it a challenging solution. The adoption of artificial intelligence for automated production inspection is now transforming the manufacturing industry.
How it works?
Embedded intelligent devices, including industrial cameras and Jetson Nano edge devices, play a crucial role in capturing and processing image and video data for deployment in factory automation production lines. The collected images from various angles undergo rapid pre-processing steps such as noise reduction, visual enhancement, out-of-focus repair, and style conversion. Subsequently, the preprocessed images are subjected to the object detection model, extracting specific elements such as models, certificates, nameplates, or cables. Simultaneously, utilizing an integrated deep learning development framework, the system continually enhances its detection capabilities through iterative distributed training, thereby improving object recognition rates.
This comprehensive approach enables the visual identification platform to swiftly, flexibly, and automatically identify various product defects during testing, including process defects, missing product logos, improperly installed nameplates, and issues related to appearance cleanliness. Notably, the visual recognition platform exhibits remarkable adaptability to non-standard change factors. Even when detection content or the environment undergoes alterations, the system quickly adapts, eliminating the need for time-consuming reconfiguration. This adaptability ensures the platform’s efficacy in addressing evolving challenges in the production environment.
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