Component Mounting Image Retention Guided by Recognition Error Rate
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Solution Overview
Problem
In component mounting systems, the storage of image data captured by cameras is inefficient due to limited storage capacity and transmission speed, and there is a need to maintain robust image processing while reducing the burden on storage resources.
Innovation Solution
A component mounting system that includes a component supply section, a pickup nozzle, an imaging section, a memory section, an image processing execution section, an error rate calculating section, a storage period monitoring section, and a storage control section, which allows for efficient storage of image data by determining the storage period based on the error rate calculated from a predetermined number of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all captured image data is stored in the storage device, then the image data can be used for various purposes such as image processing and optimization of mounting work, but the storage capacity of the storage device and transmission speed are exceeded
Solution Approach 1:
The patent applies local quality by differentiating storage priorities among image data based on their utility. Images are categorized into three groups: those requiring storage for robust image processing (captured during normal operation), those requiring storage for error analysis (captured when recognition errors occur), and those that can be discarded (captured during optimization work). This selective storage approach maintains necessary image processing robustness while optimizing storage capacity utilization.
Solution Approach 2:
The patent segments image data into distinct categories based on their purpose and importance. The storage device is logically divided into sections for normal operation images, error condition images, and optimization images. This segmentation allows the system to manage storage resources efficiently by allocating space according to the specific needs of each image category, preventing storage overflow while maintaining essential functionality.
2Quantity of substance
If only images with recognition errors are stored, then storage capacity is reduced, but insufficient images are available for maintaining image processing robustness
Solution Approach 1:
The patent applies local quality by assigning different storage requirements to different operational states. Images captured during normal operation (when image processing is functioning correctly) are stored to maintain robustness, while images from error conditions are stored for diagnostic purposes. This differentiated approach ensures that both robustness maintenance and error analysis needs are met without unnecessarily consuming storage capacity.
Solution Approach 2:
The patent implements preliminary action by proactively storing images during normal operation before errors occur. This ensures that sufficient image data is available for maintaining image processing robustness and for future error analysis, rather than waiting for errors to occur and then attempting to retrieve historical data that may not be available.
3Quantity of substance
If a fixed number of images are stored regardless of error rate, then storage capacity is wasted during normal operation, but insufficient images are available when errors occur
Solution Approach 1:
The patent implements dynamics by making the storage policy adaptive rather than static. The system dynamically adjusts what images to store based on the current operational state and error rate. During normal operation with low error rates, the system stores images selectively to maintain robustness. When error rates increase, the system automatically increases storage of error-related images for analysis. This dynamic approach optimizes storage capacity utilization while ensuring adequate images are available for error analysis when needed.
Solution Approach 2:
The patent applies feedback by using error rate monitoring to control the storage policy. The system continuously monitors recognition error rates and uses this feedback to adjust image storage behavior. When error rates exceed thresholds, the system responds by storing additional images for analysis. This feedback mechanism ensures that storage capacity is used efficiently during normal operation while automatically allocating sufficient resources when errors occur.
Data Source
AI summary
Component mounting system 1 includes electronic component mounting device 10, control device 100, image processing device 110, and storage device 115. Electronic component mounting device 10 includes mounting head 26, supply device 28, and component camera 90. Electronic component mounting device 10 holds a supplied electronic component with mounting head 26, and images the held electronic component with component camera 90. Controller 102 performs image processing on captured image data with image processing device 110 to determine the acceptability and position of the electronic component (S6). Controller 102 monitors the start and end of the storage period (S10, S13) based on the magnitude relationship between the error rate calculated from the determination result information in the error rate calculation process (S8) and the reference error rate (S9). If the image data captured with component camera 90 is stored within the storage period, controller 102 stores the image data in storage device 115 (S11).