Component Label Imaging for End-to-End Traceability
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Solution Overview
Problem
The electronics manufacturing industry faces challenges in providing robust and accurate traceability for electronic components due to the lack of standard labeling formats and degradation of labels during automated processes, which complicates mass production and affects public safety.
Innovation Solution
A high-resolution traceability system (HiTrace) utilizing a cloud-based data hub and imaging systems to capture and process images of component labels and markings, employing neural networks and deep neural networks for data extraction and classification, ensuring consistent and accurate traceability data throughout the manufacturing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard labeling formats are used for component traceability, then data consistency and accuracy are improved, but the complexity of implementing and maintaining the standard across thousands of manufacturers increases
Solution Approach 1:
The patent creates a universal labeling standard (ISO/IEC 23862) that can be applied across thousands of different component manufacturers and packaging types. The standard defines common data elements, syntax rules, and formatting requirements that work universally for resistors, capacitors, ICs, and various packaging formats (trays, reels, tubes, bulk bags), eliminating the need for manufacturer-specific labeling systems while maintaining implementation feasibility through standardized procedures
2Reliability
If imaging systems capture images at multiple stages (before placement, after placement, after reflow), then traceability robustness is improved, but the time and resources required for image acquisition increase
Solution Approach 1:
The patent captures images at multiple predetermined stages in the manufacturing process (before component placement, after placement but before reflow, and after reflow). By establishing these capture points in advance as part of the standardized traceability system, the system ensures comprehensive traceability data is collected at critical junctures without requiring real-time decision-making or additional processing time during production
Solution Approach 2:
The system uses captured images to verify component identity and track components through the manufacturing process. The imaging data provides feedback that confirms proper component placement and allows detection of any deviations or errors, enabling corrective actions while maintaining the standardized workflow and time constraints
3Measurement precision
If neural networks and deep neural networks are used for data extraction from labels, then data extraction accuracy is improved, but the processing complexity and computational resources required increase
Solution Approach 1:
The patent employs neural networks and deep neural networks as intermediary processing layers between the raw label images and the final traceability data. These AI models serve as mediators that automatically interpret various labeling formats, extract relevant data elements, and transform them into standardized traceability records, reducing the need for complex manual parsing rules while improving accuracy across diverse label types
4Measurement precision
If the system processes features and extracted logistical data to determine internal consistency and consistency with production line data, then traceability accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs consistency verification by comparing extracted logistical data against production line assembly setup data and internal logic rules. This feedback mechanism validates traceability data accuracy by detecting inconsistencies such as mismatched component identifiers, incorrect packaging configurations, or deviations from expected manufacturing parameters, ensuring high traceability accuracy while operating within production time constraints
Data Source
AI summary
A system for monitoring for an electronic circuit production line, the system comprising: at least one imaging system operable to acquire an image of a packaging unit (PU) label labeling a PU in which an electronic component that the production line includes in an electronic circuit is packaged by a supplier for delivery to an enterprise that operates the production line, and during production of the circuit at least one image of the component; and a processing and data hub having software configured to process the images to: determine a PU data record having logistical data fields comprising logistical data characterizing the PU and the component packaged in the PU; determine an encapsulation data record having logistical data fields comprising logistical data characterizing an encapsulation package of the component; and store the PU and encapsulation data records in a memory.