Printhead Pixel Dropout Detection via Image Intensity Analysis
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Solution Overview
Problem
Pixel dropout in printheads, such as thermal and inkjet printheads, results in defective printed substrates, leading to increased costs due to the need for remaking substrates, as existing detection methods are not efficient in identifying the issue promptly.
Innovation Solution
A method involving the analysis of captured images of printed substrates or print ribbons using a mechanical image capture device and processing device to generate datasets of integrated intensity values, identifying delta function-like discontinuities that indicate potential pixel dropout, and generating an alert signal for further investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing detection methods are used, then pixel dropout is not detected promptly, but implementation complexity is low
Solution Approach 1:
The image is divided into multiple rows and columns, with intensity values calculated for each row and column separately. This segmentation allows efficient processing of large images by breaking them into manageable segments that can be analyzed independently for dropout detection.
Solution Approach 2:
The patent replaces complex mechanical or manual inspection methods with an automated image processing system that uses intensity value analysis. This substitution enables prompt detection while maintaining relatively simple implementation through software-based image analysis.
2Reliability
If pixel dropout is not detected early, then number of defective substrates increases, but detection method complexity is low
Solution Approach 1:
The system performs detection immediately after printing by capturing and analyzing the image right away. This preliminary action ensures that pixel dropout is identified before substrates are processed further or stacked, enabling timely detection while using a relatively simple intensity analysis approach.
Solution Approach 2:
The system provides immediate feedback by analyzing intensity values and identifying dropout patterns in real-time. This feedback mechanism allows for prompt detection of defective print elements, improving substrate quality assurance without requiring complex multi-step verification processes.
3Loss of information
If substrates are remade due to undetected pixel dropout, then costs increase, but detection capability is insufficient
Solution Approach 1:
The patent replaces insufficient manual or basic detection capabilities with an automated image processing system that calculates intensity values and identifies dropout patterns algorithmically. This substitution significantly improves defect detection capability while maintaining relatively simple implementation through software-based analysis.
Solution Approach 2:
The system creates a digital copy of the printed substrate through image capture and processes this copy for defect detection. This copying approach enables thorough analysis of substrate quality without requiring physical inspection, improving detection capability while keeping the system relatively simple.
Data Source
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AI summary
A technique is described for determining pixel dropout in a printhead that has a plurality of print elements arrayed along an axis. In the technique, a dataset of integrated intensity values, in the printing direction on a substrate, of a captured image is generated and used to determine if pixel dropout has or may have occurred.